m tech thesis topics in data mining

82 Data Mining Essay Topic Ideas & Examples

🏆 best data mining topic ideas & essay examples, 💡 good essay topics on data mining, ✅ most interesting data mining topics to write about.

Disadvantages of Using Web 2.0 for Data Mining Applications This data can be confusing to the readers and may not be reliable. Lastly, with the use of Web 2.
Data Mining and Its Major Advantages Thus, it is possible to conclude that data mining is a convenient and effective way of processing information, which has many advantages.
The Data Mining Method in Healthcare and Education Thus, I would use data mining in both cases; however, before that, I would discover a way to improve the algorithms used for it.
Data Mining Tools and Data Mining Myths The first problem is correlated with keeping the identity of the person evolved in data mining secret. One of the major myths regarding data mining is that it can replace domain knowledge.
Hybrid Data Mining Approach in Healthcare One of the healthcare projects that will call for the use of data mining is treatment evaluation. In this case, it is essential to realize that the main aim of health data mining is to […]
Terrorism and Data Mining Algorithms However, this is a necessary evil as the nation’s security has to be prioritized since these attacks lead to harm to a larger population compared to the infringements.
Transforming Coded and Text Data Before Data Mining However, to complete data mining, it is necessary to transform the data according to the techniques that are to be used in the process.
Data Mining and Machine Learning Algorithms The shortest distance of string between two instances defines the distance of measure. However, this is also not very clear as to which transformations are summed, and thus it aims to a probability with the […]
Summary of C4.5 Algorithm: Data Mining 5 algorism: Each record from set of data should be associated with one of the offered classes, it means that one of the attributes of the class should be considered as a class mark.
Data Mining in Social Networks: Linkedin.com One of the ways to achieve the aim is to understand how users view data mining of their data on LinkedIn.
Ethnography and Data Mining in Anthropology The study of cultures is of great importance under normal circumstances to enhance the understanding of the same. Data mining is the success secret of ethnography.
Issues With Data Mining It is necessary to note that the usage of data mining helps FBI to have access to the necessary information for terrorism and crime tracking.
Large Volume Data Handling: An Efficient Data Mining Solution Data mining is the process of sorting huge amount of data and finding out the relevant data. Data mining is widely used for the maintenance of data which helps a lot to an organization in […]
Data Mining and Analytical Developments In this era where there is a lot of information to be handled at ago and actually with little available time, it is necessarily useful and wise to analyze data from different viewpoints and summarize […]
Levi’s Company’s Data Mining & Customer Analytics Levi, the renowned name in jeans is feeling the heat of competition from a number of other brands, which have come upon the scene well after Levi’s but today appear to be approaching Levi’s market […]
Cryptocurrency Exchange Market Prediction and Analysis Using Data Mining and Artificial Intelligence This paper aims to review the application of A.I.in the context of blockchain finance by examining scholarly articles to determine whether the A.I.algorithm can be used to analyze this financial market.
“Data Mining and Customer Relationship Marketing in the Banking Industry“ by Chye & Gerry First of all, the article generally elaborates on the notion of customer relationship management, which is defined as “the process of predicting customer behavior and selecting actions to influence that behavior to benefit the company”.
Data Mining Techniques and Applications The use of data mining to detect disturbances in the ecosystem can help to avert problems that are destructive to the environment and to society.
Ethical Data Mining in the UAE Traffic Department The research question identified in the assignment two is considered to be the following, namely whether the implementation of the business intelligence into the working process will beneficially influence the work of the Traffic Department […]
Canadian University Dubai and Data Mining The aim of mining data in the education environment is to enhance the quality of education for the mass through proactive and knowledge-based decision-making approaches.
Data Mining and Customer Relationship Management As such, CRM not only entails the integration of marketing, sales, customer service, and supply chain capabilities of the firm to attain elevated efficiencies and effectiveness in conveying customer value, but it obliges the organization […]
E-Commerce: Mining Data for Better Business Intelligence The method allowed the use of Intel and an example to build the study and the literature on data mining for business intelligence to analyze the findings.
Ethical Implications of Data Mining by Government Institutions Critics of personal data mining insist that it infringes on the rights of an individual and result to the loss of sensitive information.
Data Mining Role in Companies The increasing adoption of data mining in various sectors illustrates the potential of the technology regarding the analysis of data by entities that seek information crucial to their operations.
Data Warehouse and Data Mining in Business The circumstances leading to the establishment and development of the concept of data warehousing was attributed to the fact that failure to have a data warehouse led to the need of putting in place large […]
Data Mining: Concepts and Methods Speed of data mining process is important as it has a role to play in the relevance of the data mined. The accuracy of data is also another factor that can be used to measure […]
Data Mining Technologies According to Han & Kamber, data mining is the process of discovering correlations, patterns, trends or relationships by searching through a large amount of data that in most circumstances is stored in repositories, business databases […]
Data Mining: A Critical Discussion In recent times, the relatively new discipline of data mining has been a subject of widely published debate in mainstream forums and academic discourses, not only due to the fact that it forms a critical […]
Commercial Uses of Data Mining Data mining process entails the use of large relational database to identify the correlation that exists in a given data. The principal role of the applications is to sift the data to identify correlations.
A Discussion on the Acceptability of Data Mining Today, more than ever before, individuals, organizations and governments have access to seemingly endless amounts of data that has been stored electronically on the World Wide Web and the Internet, and thus it makes much […]
Applying Data Mining Technology for Insurance Rate Making: Automobile Insurance Example
Applebee’s, Travelocity and Others: Data Mining for Business Decisions
Applying Data Mining Procedures to a Customer Relationship
Business Intelligence as Competitive Tool of Data Mining
Overview of Accounting Information System Data Mining
Applying Data Mining Technique to Disassembly Sequence Planning
Approach for Image Data Mining Cultural Studies
Apriori Algorithm for the Data Mining of Global Cyberspace Security Issues
Database Data Mining: The Silent Invasion of Privacy
Data Management: Data Warehousing and Data Mining
Constructive Data Mining: Modeling Consumers’ Expenditure in Venezuela
Data Mining and Its Impact on Healthcare
Innovations and Perspectives in Data Mining and Knowledge Discovery
Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection
Linking Data Mining and Anomaly Detection Techniques
Data Mining and Pattern Recognition Models for Identifying Inherited Diseases
Credit Card Fraud Detection Through Data Mining
Data Mining Approach for Direct Marketing of Banking Products
Constructive Data Mining: Modeling Argentine Broad Money Demand
Data Mining-Based Dispatching System for Solving the Pickup and Delivery Problem
Commercially Available Data Mining Tools Used in the Economic Environment
Data Mining Climate Variability as an Indicator of U.S. Natural Gas
Analysis of Data Mining in the Pharmaceutical Industry
Data Mining-Driven Analysis and Decomposition in Agent Supply Chain Management Networks
Credit Evaluation Model for Banks Using Data Mining
Data Mining for Business Intelligence: Multiple Linear Regression
Cluster Analysis for Diabetic Retinopathy Prediction Using Data Mining Techniques
Data Mining for Fraud Detection Using Invoicing Data
Jaeger Uses Data Mining to Reduce Losses From Crime and Waste
Data Mining for Industrial Engineering and Management
Business Intelligence and Data Mining – Decision Trees
Data Mining for Traffic Prediction and Intelligent Traffic Management System
Building Data Mining Applications for CRM
Data Mining Optimization Algorithms Based on the Swarm Intelligence
Big Data Mining: Challenges, Technologies, Tools, and Applications
Data Mining Solutions for the Business Environment
Overview of Big Data Mining and Business Intelligence Trends
Data Mining Techniques for Customer Relationship Management
Classification-Based Data Mining Approach for Quality Control in Wine Production
Data Mining With Local Model Specification Uncertainty
Employing Data Mining Techniques in Testing the Effectiveness of Modernization Theory
Enhancing Information Management Through Data Mining Analytics
Evaluating Feature Selection Methods for Learning in Data Mining Applications
Extracting Formations From Long Financial Time Series Using Data Mining
Financial and Banking Markets and Data Mining Techniques
Fraudulent Financial Statements and Detection Through Techniques of Data Mining
Harmful Impact Internet and Data Mining Have on Society
Informatics, Data Mining, Econometrics, and Financial Economics: A Connection
Integrating Data Mining Techniques Into Telemedicine Systems
Investigating Tobacco Usage Habits Using Data Mining Approach
Electronics Engineering Paper Topics
Cyber Security Topics
Google Paper Topics
Hacking Essay Topics
Identity Theft Essay Ideas
Internet Research Ideas
Microsoft Topics
Chicago (A-D)
Chicago (N-B)

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Student theses

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3d face reconstruction using deep learning.

Supervisor: Medeiros de Carvalho, R. (Supervisor 1), Gallucci, A. (Supervisor 2) & Vanschoren, J. (Supervisor 2)

Student thesis : Master

Achieving Long Term Fairness through Curiosity Driven Reinforcement Learning: How intrinsic motivation influences fairness in algorithmic decision making

Supervisor: Pechenizkiy, M. (Supervisor 1), Gajane, P. (Supervisor 2) & Kapodistria, S. (Supervisor 2)

Activity Recognition Using Deep Learning in Videos under Clinical Setting

Supervisor: Duivesteijn, W. (Supervisor 1), Papapetrou, O. (Supervisor 2), Zhang, L. (External coach) & Vasu, J. D. (External coach)

A Data Cleaning Assistant

Supervisor: Vanschoren, J. (Supervisor 1)

Student thesis : Bachelor

A Data Cleaning Assistant for Machine Learning

A deep learning approach for clustering a multi-class dataset.

Supervisor: Pei, Y. (Supervisor 1), Marczak, M. (External coach) & Groen, J. (External coach)

Aerial Imagery Pixel-level Segmentation

A framework for understanding business process remaining time predictions.

Supervisor: Pechenizkiy, M. (Supervisor 1) & Scheepens, R. J. (Supervisor 2)

A Hybrid Model for Pedestrian Motion Prediction

Supervisor: Pechenizkiy, M. (Supervisor 1), Muñoz Sánchez, M. (Supervisor 2), Silvas, E. (External coach) & Smit, R. M. B. (External coach)

Algorithms for center-based trajectory clustering

Supervisor: Buchin, K. (Supervisor 1) & Driemel, A. (Supervisor 2)

Allocation Decision-Making in Service Supply Chain with Deep Reinforcement Learning

Supervisor: Zhang, Y. (Supervisor 1), van Jaarsveld, W. L. (Supervisor 2), Menkovski, V. (Supervisor 2) & Lamghari-Idrissi, D. (Supervisor 2)

Analyzing Policy Gradient approaches towards Rapid Policy Transfer

An empirical study on dynamic curriculum learning in information retrieval.

Supervisor: Fang, M. (Supervisor 1)

An Explainable Approach to Multi-contextual Fake News Detection

Supervisor: Pechenizkiy, M. (Supervisor 1), Pei, Y. (Supervisor 2) & Das, B. (External coach)

An exploration and evaluation of concept based interpretability methods as a measure of representation quality in neural networks

Supervisor: Menkovski, V. (Supervisor 1) & Stolikj, M. (External coach)

Anomaly detection in image data sets using disentangled representations

Supervisor: Menkovski, V. (Supervisor 1) & Tonnaer, L. M. A. (Supervisor 2)

Anomaly Detection in Polysomnography signals using AI

Supervisor: Pechenizkiy, M. (Supervisor 1), Schwanz Dias, S. (Supervisor 2) & Belur Nagaraj, S. (External coach)

Anomaly detection in text data using deep generative models

Supervisor: Menkovski, V. (Supervisor 1) & van Ipenburg, W. (External coach)

Anomaly Detection on Dynamic Graph

Supervisor: Pei, Y. (Supervisor 1), Fang, M. (Supervisor 2) & Monemizadeh, M. (Supervisor 2)

Anomaly Detection on Finite Multivariate Time Series from Semi-Automated Screwing Applications

Supervisor: Pechenizkiy, M. (Supervisor 1) & Schwanz Dias, S. (Supervisor 2)

Anomaly Detection on Multivariate Time Series Using GANs

Supervisor: Pei, Y. (Supervisor 1) & Kruizinga, P. (External coach)

Anomaly detection on vibration data

Supervisor: Hess, S. (Supervisor 1), Pechenizkiy, M. (Supervisor 2), Yakovets, N. (Supervisor 2) & Uusitalo, J. (External coach)

Application of P&ID symbol detection and classification for generation of material take-off documents (MTOs)

Supervisor: Pechenizkiy, M. (Supervisor 1), Banotra, R. (External coach) & Ya-alimadad, M. (External coach)

Applications of deep generative models to Tokamak Nuclear Fusion

Supervisor: Koelman, J. M. V. A. (Supervisor 1), Menkovski, V. (Supervisor 2), Citrin, J. (Supervisor 2) & van de Plassche, K. L. (External coach)

A Similarity Based Meta-Learning Approach to Building Pipeline Portfolios for Automated Machine Learning

Aspect-based few-shot learning.

Supervisor: Menkovski, V. (Supervisor 1)

Assessing Bias and Fairness in Machine Learning through a Causal Lens

Supervisor: Pechenizkiy, M. (Supervisor 1)

Assessing fairness in anomaly detection: A framework for developing a context-aware fairness tool to assess rule-based models

Supervisor: Pechenizkiy, M. (Supervisor 1), Weerts, H. J. P. (Supervisor 2), van Ipenburg, W. (External coach) & Veldsink, J. W. (External coach)

A Study of an Open-Ended Strategy for Learning Complex Locomotion Skills

A systematic determination of metrics for classification tasks in openml, a universally applicable emm framework.

Supervisor: Duivesteijn, W. (Supervisor 1), van Dongen, B. F. (Supervisor 2) & Yakovets, N. (Supervisor 2)

Automated machine learning with gradient boosting and meta-learning

Automated object recognition of solar panels in aerial photographs: a case study in the liander service area.

Supervisor: Pechenizkiy, M. (Supervisor 1), Medeiros de Carvalho, R. (Supervisor 2) & Weelinck, T. (External coach)

Automatic data cleaning

Automatic scoring of short open-ended questions.

Supervisor: Pechenizkiy, M. (Supervisor 1) & van Gils, S. (External coach)

Automatic Synthesis of Machine Learning Pipelines consisting of Pre-Trained Models for Multimodal Data

Automating string encoding in automl, autoregressive neural networks to model electroencephalograpy signals.

Supervisor: Vanschoren, J. (Supervisor 1), Pfundtner, S. (External coach) & Radha, M. (External coach)

Balancing Efficiency and Fairness on Ride-Hailing Platforms via Reinforcement Learning

Supervisor: Tavakol, M. (Supervisor 1), Pechenizkiy, M. (Supervisor 2) & Boon, M. A. A. (Supervisor 2)

Benchmarking Audio DeepFake Detection

Better clustering evaluation for the openml evaluation engine.

Supervisor: Vanschoren, J. (Supervisor 1), Gijsbers, P. (Supervisor 2) & Singh, P. (Supervisor 2)

Bi-level pipeline optimization for scalable AutoML

Supervisor: Nobile, M. (Supervisor 1), Vanschoren, J. (Supervisor 1), Medeiros de Carvalho, R. (Supervisor 2) & Bliek, L. (Supervisor 2)

Block-sparse evolutionary training using weight momentum evolution: training methods for hardware efficient sparse neural networks

Supervisor: Mocanu, D. (Supervisor 1), Zhang, Y. (Supervisor 2) & Lowet, D. J. C. (External coach)

Boolean Matrix Factorization and Completion

Supervisor: Peharz, R. (Supervisor 1) & Hess, S. (Supervisor 2)

Bootstrap Hypothesis Tests for Evaluating Subgroup Descriptions in Exceptional Model Mining

Supervisor: Duivesteijn, W. (Supervisor 1) & Schouten, R. M. (Supervisor 2)

Bottom-Up Search: A Distance-Based Search Strategy for Supervised Local Pattern Mining on Multi-Dimensional Target Spaces

Supervisor: Duivesteijn, W. (Supervisor 1), Serebrenik, A. (Supervisor 2) & Kromwijk, T. J. (Supervisor 2)

Bridging the Domain-Gap in Computer Vision Tasks

Supervisor: Mocanu, D. C. (Supervisor 1) & Lowet, D. J. C. (External coach)

CCESO: Auditing AI Fairness By Comparing Counterfactual Explanations of Similar Objects

Supervisor: Pechenizkiy, M. (Supervisor 1) & Hoogland, K. (External coach)

Clean-Label Poison Attacks on Machine Learning

Supervisor: Michiels, W. P. A. J. (Supervisor 1), Schalij, F. D. (External coach) & Hess, S. (Supervisor 2)

M.Tech/Ph.D Thesis Help in Chandigarh | Thesis Guidance in Chandigarh

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Data Mining is the process of transforming unprocessed data to useful one by use certain methodologies and tactics. Data Mining involves discovering and identifying patterns in large data sets which is used by large companies to anticipate the future trends.

Latest thesis topics in data mining for research scholars:

Performance enhancement of DBSCAN density based clustering algorithm in data mining
The classification scheme for sentiment analysis of twitter data
The classification scheme for credit card fraud detection in Data mining
To propose novel technique for the crime rate prediction in Data Mining
To evaluate and propose heart disease prediction scheme in Data Mining
The diabetes prediction technique for Data mining using classification
Novel Algorithm for the network traffic classification in Data Mining
To design voting based classification method for the student performance evaluation
The hybrid classification method for the fake news detection in data mining.
To propose novel classification method for insurance policy fraud detection

What is a data set?

A data set is a collection of similar data. We can also refer data set as a single database. In a data set, the data is stored in an organized form which can be accessed by applying some logic. Following are the types of data set;

File-based data set

Folder based data set

Database data set

Web-based data set

Process of Data Mining

Data Mining is a comparatively new technology to determine the futuristic trends. Data Mining tends to extract out valuable information from large unused data using statistical techniques or by using techniques of artificial intelligence and machine learning. The extracted data can be used to increase the sales, grow the business, to analyze the market trends and also in fraud detection. Students working on Ph.D. thesis in Data Mining can explain about the process in their work. Data mining is a repetitive process and it goes through the following phases as given by Cross Industry Standard Process for data mining (CRISP-DM) process model:

Problem definition – In the first phase, the business objectives and needs are determined based on the current scenario. Its requirements are studied and then an evaluation plan is prepared taking into consideration various assumptions, constraints, and conditions.

Data understanding and exploration – In this phase, the available data is collected and explored. While exploring, the experts identify the underlying problems with data using certain statistical methods. The quality of data is also checked in this phase.

Data preparation – Once the raw data is collected, it is selected, cleansed and formatted in a desired way. The data is then prepared for modeling by selecting tables, records, cases, and attributes. While preparing, the meaning of data is not at all changed.

Modeling – In this phase various modeling techniques are applied to the prepared data including mining functions and a model is created. After the model is created, it goes through testing to verify and validate the model. Some other models are also generated using modeling tools. The models are then accessed in the presence of expertise to check whether it meets business requirements or not.

Evaluation – After the model is created, it is evaluated by a team of experts to verify it in terms of business objectives. It don’t satisfy the needs then it again goes through the modeling phase. After the successful completion of this phase, the use of data mining results is decided by the experts.

Deployment – In this phase, the plans for deployment, maintenance, and monitoring is prepared for implementation. A properly organized report of data mining is prepared which will be a summary of the whole process

Data Mining Techniques

Following are some of the data mining techniques used for data mining process:

Association – In this technique, a pattern is identified based on the relationship between items of similar proceedings. A customer behavior can be analyzed by an analyst using association technique based on his buying patterns.

Classification – This technique of data mining is based on machine learning using the concepts of decision trees, linear programming, neural networks, and statistics. In this items are classified into predefined groups and classes. This method depends upon predictions made using predefined techniques.

Clustering – Clustering is the process of making a cluster of abstract objects having similar characteristics. Clustering technique is used in Machine Learning, Image Analysis, Pattern Recognition, and retrieving information.

Decision Trees – It is a graphical technique of data mining in which root of the tree is a condition and its branches are its solutions. This technique of Data Mining is used in Machine Learning.

Prediction – This data mining technique identifies the relationship between independent and dependent variables and is mainly used in predicting the future for a sale.It is an important technique of data mining in which repetitive pattern is recognized in intelligent environments. It helps in predicting future events.

Sequential Analysis – Sequential analysis is a technique that discovers and identifies similar patterns, events, and trends in transactional data over a certain period of time.

Examples of Data Mining

There are various real-life examples of data mining from everyday life. The most common example for this is cross-selling by e-commerce sites based on the searches made by the customer on the web. Another example for this is the loyalty card programme run by various stores and markets to gather valuable customer information. Fraud detection, particularly in the field of telecommunication and card sale service, is another example for this. Data mining helps in determining duration, location and time of the call in case of fraud calls.

Data Mining Trends

Data mining is used in wide range of areas from telecommunication to financial areas. It is also being taught as a subject in various colleges as a part of the curriculum, particularly in computer science. For masters students, this is a very good thesis topic as well as for research. Numerous agencies are available over the Internet that will provide thesis writing assistance and help for data mining. It is a relatively new technology and yet to reach a wider audience.

Applications of Data Mining

In Medical Science

A lot of data is generated in medical science every day which needs to be managed. Data Mining is useful in this case for extracting valuable information from this data thus generated. Data Mining is helpful in medical science to:

Detect frauds in hospitals and medical centers
Explore the business more effectively
Analyse patient’s health by monitoring his day to day activities
For successful treatment of a patient’s health

In Banking/Finance

Data Mining can be used to analyze customer behavior by tracking his different purchases and daily activities. We can get information about how much does a customer spends using his credit card and which product he usually buys.

In Marketing and Sales

Data Mining is very helpful, particularly in marketing and sales business. Through data mining, marketing and sales enterprises can make offers to customers based on their purchases and also on what product he usually searches.

In Science and Engineering

Data Mining also finds its application in the field of science and engineering for the development of new products like sensor devices and pattern recognition system. Data Mining also finds its application in Machine Learning, pattern recognition, database management and artificial intelligence.

Thesis, Project and Research Ideas/Topics in Data Mining

Following is the list of data mining thesis ideas and research topics:

Data Leakage Detection
Database Text Mining
Web Content Analysis
Social Media Mining
Climate Change Study using Data Mining
Weather Forecasting using Data Mining
Opinion Mining
Enterprise Resource Planning
Stock Market Analysis

Web Mining is an application of Data Mining and an important topic for research and thesis. It is a technique to discover patterns from WWW i.e World Wide Web. The information for web mining is collected through browser activities, page content and server logins. It is a very good area for master thesis data mining. There are three types of Web Mining:

Web Usage Mining

Web Content Mining

Web Structure Mining

It is a technique to extract usage patterns from Web Data. These patterns are used for understanding the needs of Web-based applications. Web usage mining can also be classified according to the following type of data:

Web Server Data
Application Server Data
Application Level Data

Web Content Mining refers to the extraction of useful information and data from Web Page content. For retrieving information from the web page intelligent tools like web agents are used. Intelligent Systems are created which involve this agent-based approach.

In this technique, graph theory is used for analyzing the node and structure of the website. It can be classified into two different types :

Identifying and extracting patterns from a hyperlink
Document structure mining – describing HTML and XML tag usage.

Text Mining

It is an important field of Data Mining. It refers to the process of extracting valuable information from text and is also referred to as text analytics. This high-quality information is extracted through patterns and methods like statistical pattern learning. It is another good area for the Ph.D. thesis on Data Mining. In Text Mining, input data is structured and patterns are derived from this structured data. There are various research areas and thesis topics in the field of text mining.

Applications of Text Mining

Following are the main application areas of Text Mining:

Competitive Intelligence
Security Applications like encryption and decryption
Biomedical Applications for biomedical text mining
Software Applications
Business and marketing applications
Academic Applications

For any thesis help on data mining, contact us . Techsparks provides thesis guidance in data mining.

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How does data mining work?

A standard data mining design begins with the appropriate business statement in the questionnaire, the appropriate data is collected to tackle it, and the data is prepared for the examination.
What happens in the earlier stages determines how successful the later versions are.
Data miners should assure the data quality they utilize as input for research because bad data quality results in poor outcomes.
Establishing a detailed understanding of the design factors, such as the present business scenario, the project’s main business goal, and the performance objectives.
Identifying the data required to address the problem as well as collecting this from all sorts of sources.
Addressing any errors and bugs, like incomplete or duplicate data, and processing the data in a suitable format to solve the research questions.
Algorithms are used to find patterns from data.
Identifying if or how another model’s output will contribute to the achievement of a business objective.
In order to acquire the optimum outcome, an iterative process is frequently used to identify the best method.
Getting the project’s findings suitable for making decisions in real-time

The techniques and actions listed above are repeated until the best outcomes are achieved. Our engineers and developers have extensive knowledge of the tools, techniques, and approaches used in the processes described above. We guarantee that we will provide the best research advice w.r.t to data mining thesis topics and complete your project on schedule. What are the important data mining tasks?

Data Mining Tasks

Data mining finds application in many ways including description, Analysis, summarization of data, and clarifying the conceptual understanding by data description
And also prediction, classification, dependency analysis, segmentation, and case-based reasoning are some of the important data mining tasks
Regression – numerical data prediction (stock prices, temperatures, and total sales)
Data warehousing – business decision making and large-scale data mining
Classification – accurate prediction of target classes and their categorization
Association rule learning – market-based analytical tools that were involved in establishing variable data set relationship
Machine learning – statistical probability-based decision making method without complicated programming
Data analytics – digital data evaluation for business purposes
Clustering – dataset partitioning into clusters and subclasses for analyzing natural data structure and format
Artificial intelligence – human-based Data analytics for reasoning, solving problems, learning, and planning
Data preparation and cleansing – conversion of raw data into a processed form for identification and removal of errors

You can look at our website for a more in-depth look at all of these operations. We supply you with the needed data, as well as any additional data you may need for your data mining thesis topics . We supply non-plagiarized data mining thesis assistance in any fresh idea of your choice. Let us now discuss the stages in data mining that are to be included in your thesis topics

How to work on a data mining thesis topic?

The following are the important stages or phases in developing data mining thesis topics.

First of all, you need to identify the present demand and address the question
The next step is defining or specifying the problem
Collection of data is the third step
Alternative solutions and designs have to be analyzed in the next step
The proposed methodology has to be designed
The system is then to be implemented

Usually, our experts help in writing codes and implementing them successfully without hassles . By consistently following the above steps you can develop one of the best data mining thesis topics of recent days. Furthermore, technically it is important for you to have a better idea of all the tasks and techniques involved in data mining about which we have discussed below

Data visualization
Neural networks
Statistical modeling
Genetic algorithms and neural networks
Decision trees and induction
Discriminant analysis
Induction techniques
Association rules and data visualization
Bayesian networks
Correlation
Regression analysis
Regression analysis and regression trees

If you are looking forward to selecting the best tool for your data mining project then evaluating its consistency and efficiency stands first. For this, you need to gain enough technical data from real-time executed projects for which you can directly contact us. Since we have delivered an ample number of data mining thesis topics successfully we can help you in finding better solutions to all your research issues. What are the points to be remembered about the data mining strategy?

Furthermore, data mining strategies must be picked before instruments in order to prevent using strategies that do not align with the article’s true purposes.
The typical data mining strategy has always been to evaluate a variety of methodologies in order to select one which best fits the situation.
As previously said, there are some principles that may be used to choose effective strategies for data mining projects.
Since they are easy to handle and comprehend
They could indeed collaborate with definitional and parametric data
Tare unaffected by critical values, they could perhaps function with incomplete information
They could also expose various interrelationships and an absence of linear combinations
They could indeed handle noise in records
They can process huge amounts of data.
Decision trees, on the other hand, have significant drawbacks.
Many rules are frequently necessary for dependent variables or numerous regressions, and tiny changes in the data can result in very different tree architectures.

All such pros and cons of various data mining aspects are discussed on our website. We will provide you with high-quality research assistance and thesis writing assistance . You may see proof of our skill and the unique approach that we generated in the field by looking at the samples of the thesis that we produced on our website. We also offer an internal review to help you feel more confident. Let us now discuss the recent data mining methodologies

Current methods in Data Mining

Prediction of data (time series data mining)
Discriminant and cluster analysis
Logistic regression and segmentation

Our technical specialists and technicians usually give adequate accurate data, a thorough and detailed explanation, and technical notes for all of these processes and algorithms. As a result, you can get all of your questions answered in one spot. Our technical team is also well-versed in current trends, allowing us to provide realistic explanations for all new developments. We will now talk about the latest data mining trends

Latest Trending Data Mining Thesis Topics

Visual data mining and data mining software engineering
Interaction and scalability in data mining
Exploring applications of data mining
Biological and visual data mining
Cloud computing and big data integration
Data security and protecting privacy in data mining
Novel methodologies in complex data mining
Data mining in multiple databases and rationalities
Query language standardization in data mining
Integration of MapReduce, Amazon EC2, S3, Apache Spark, and Hadoop into data mining

These are the recent trends in data mining. We insist that you choose one of the topics that interest you the most. Having an appropriate content structure or template is essential while writing a thesis . We design the plan in a chronological order relevant to the study assessment with this in mind. The incorporation of citations is one of the most important aspects of the thesis. We focus not only on authoring but also on citing essential sources in the text. Students frequently struggle to deal with appropriate proposals when commencing their thesis. We have years of experience in providing the greatest study and data mining thesis writing services to the scientific community, which are promptly and widely acknowledged. We will now talk about future research directions of research in various data mining thesis topics

Future Research Directions of Data Mining

The potential of data mining and data science seems promising, as the volume of data continues to grow.
It is expected that the total amount of data in our digital cosmos will have grown from 4.4 zettabytes to 44 zettabytes.
We’ll also generate 1.7 gigabytes of new data for every human being on this planet each second.
Mining algorithms have completely transformed as technology has advanced, and thus have tools for obtaining useful insights from data.
Only corporations like NASA could utilize their powerful computers to examine data once upon a time because the cost of producing and processing data was simply too high.
Organizations are now using cloud-based data warehouses to accomplish any kinds of great activities with machine learning, artificial intelligence, and deep learning.

The Internet of Things as well as wearable electronics, for instance, has transformed devices to be connected into data-generating engines which provide limitless perspectives into people and organizations if firms can gather, store, and analyze the data quickly enough. What are the aspects to be remembered for choosing the best data mining thesis topics?

An excellent thesis topic is a broad concept that has to be developed, verified, or refuted.
Your thesis topic must capture your curiosity, as well as the involvement of both the supervisor and the academicians.
Your thesis topic must be relevant to your studies and should be able to withstand examination.

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Trending Data Mining Research Thesis Topics

Research Topics in Data Mining

Handling cost-effective, unbalanced non-static data
Issues related to data mining and their solutions
Network settings in data mining and ensuring privacy, security, and integrity of data
Environmental and biological issues in data mining
Complex data mining and sequential data mining (time series data)
Data mining at higher dimensions
Multi-agent data mining and distributed data mining
High-speed data mining
Development of unified data mining theory

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How to choose a good thesis topic in Data Mining?

I have seen many people asking for help in data mining forums and on other websites about how to choose a good thesis topic in data mining . Therefore, in this this post, I will address this question .

The first thing to consider is whether you want to design/improve data mining techniques , apply data mining techniques or do both. Personally, I think that designing or improving data mining techniques is more challenging than using already existing techniques. Moreover, you can make a more fundamental contribution if you work on improving data mining techniques instead of applying them. However, you need to be aware that improving data mining techniques may require better algorithmic and/or mathematics skills.

The second thing to consider is what kind of techniques you want to apply or design/improve? Data mining is a broad field consisting of many techniques such as neural networks, association rule mining algorithms, clustering and outlier detection. You should try to get some overview of the different techniques to see what you are more interested in. To get a rough overview of the field, you could read some introduction books on data mining such as the book by Tan, Steinbach & Kumar ( Introduction to data mining ) or read websites and articles related to data mining. If your goal is just to apply data mining techniques to achieve some other purpose (e.g. analysing cancer data) but you don’t know which one yet, you could skip this question.

The third thing to consider is which problems you want to solve or what you want to improve. This requires more thoughts. A good way is to look at recent good data mining conferences (KDD, ICDM, PKDD, PAKDD, ADMA, DAWAK, etc.) and journals (TKDE, TKDD, KAIS, etc.), or to attend conferences, if possible, and talk with other researchers. This helps to see what are the current popular topics and what kind of problems researchers are currently trying to solve. It does not mean that you need to work on the most popular topic. Working on a popular topic (e.g. social network mining) has several advantages. It is easier to get grants or in some case to get your papers accepted in special issues, workshops, etc. However, there are also some “older” topics that are also interesting even if they are not the current flavor of the day. Actually, the most important is that you find a topic that you like and will enjoy working on it for perhaps a few years of your life. Finding a good problem to work on can require to read several articles to understand what are the limitations of current techniques and decide what can be improved. So don’t worry. It is normal that it takes time to find a more specific topic.

Fourth, one should not forget that helping to choose a thesis topic is also the job of the professor that supervise the Master or Ph.D Students . Therefore, if you are looking for a thesis topic , it is good to talk with your supervisor and ask for suggestions. He should help you. If you don’t have a supervisor yet, then try to get a rough idea of what you like, and try to meet/discuss with professors that could become your supervisors. Some of them will perhaps have some research projects and ideas that they could give you if you work with them. Choosing a supervisor is a very important and strategic decision that every graduate student has to make. For more information about choosing a supervisor, you can read this post : How to choose a research advisor for M.Sc. / Ph.D ?

Lastly, I would like to discuss the common question “ please give me a Ph.D. topic in data mining “, that I read on websites and that I sometimes receive in my e-mails. There are two problems with this question. The first problem is that it is too general . As mentioned, data mining is a very broad field. For example, I could suggest you some very specific topics such as detecting outliers in imbalanced stock market data or to optimize the memory efficiency of subgraph mining algorithms for community detection in social networks. But will you like it? It is best to choose something by yourself that you like. The second problem with the above question is that choosing a topic is the work that a researcher should do or learn to do. In fact, in research, it is equally important to be able to find a good research problem as it is to find a good solution . Therefore, I highly recommend to try to find a research topic by yourself, as it is important to develop this skill to become a successful researcher. If you are a student, when searching for a topic, you can ask your research advisor to guide you.

Also, just for fun, here is a Ph.D thesis title generator .

If you like this blog, you can subscribe to the RSS Feed or my Twitter account ( https://twitter.com/philfv ) to get notified about future blog posts.

482 responses to how to choose a good thesis topic in data mining.

Good article.

i need your guidence to select a topic for research in data mining area????

Then, I suggest to read the blog post on top of this page. It provides guidance about how to search a topic.

thank you for your guidelines

Really good one….

Sir, i want to do Ph.D on data mining but i am not a good programmer so please tell me the topic of research on data mining or any other area which need no programming skills.

plz take it urgent. Hoping for your reply at the earliest.

If you want absolutely NO programming and still do some data mining, then I think that you would have to go toward mathematics and statistics. Or you could do some applied data mining. For example, you use some already made data mining tools or software such as Matlab or R to analyze some data. The contribution would be to do something new with the data instead of proposing new algorithms.

For finding a specific topic, you can read the blog post above which explain the steps for finding a good topic.

i am interested in page ranking topic for my mtech thesis in data mining. Can you suggest me some sources from where i can get good information about the topic?

I don’t work on this topic. So just using Google Scholar could be a good idea.

Hiee mam can I get ur mail I’d I want to discuss for thesis

Yes Sir, Mr. Mohd Shahid Khan said right for those who can not do programming and the same problem with me also so please suggest those topics through which I can do my project in data mining in M-Tech as you said in his reply that “use some already made data mining tools or software such as Matlab or R to analyze some data. ” So please suggest me on this type of topic.

The goal of this blog post is to explain how to search for a topic, rather than give topic. It takes time to find a good topic, so I cannot do it for you. But you can read the blog post to understand how to search.

HI. I want to do a Ph.D. in statistical data mining. But it is difficult to choose a topic. There are too many data mining topics. I’m thinking of improving decision tree mining algorithms with statistical validity tests. Is it a good idea?

I’m not working with decision trees, but it seems like an interesting topic. What is important is that you will propose something new that has not done before and that is useful. I think that it could meet these criteria. I would recommend to read about what has been done on statistical validity tests with decision tree, to try to see what can be done. Note that, even if you choose this as your project, it is possible to change the orientation of your research later on.

Hope this helps,

Many people have already worked on extending statistics to various types of classifiers. Try to do research on Ensemble classifiers and other types. As you are interested in decision trees, I suggest you to look at the usage of emerging patterns.

Sir, i want to do Ph.D on data mining but i am unable to find any problem in data mining so please tell me the problems on data mining or any other areas. plz take it urgent. Hoping for your reply at the earliest

Data mining is very broad. I could give you some topics, for example, what about applying neural networks to recognize music, using association rules to classify medical data, improving the memory efficiency of clustering algorithms, sequential pattern mining algorithms, etc. I mean, I could suggest you a lot of topics like that. But data mining is a very broad field. It would be better that you like. Do you have any specific interests in data mining?

I am looking for a research topic within data mining. I read your this reply and I am interested in neural Networks. Please suggest me a topic and where to start looking for it ?

Thanks & Best Regards, Jyoti

If you are interested in neural networks, then I see two possibilities: improving neural networks or using the neural networks in new ways…

I’m not an expert on neural networks so i don’t know what are the current research issues on this topic. I would recommend to read the titles and abstract of articles from conferences and journals on neural networks such as http://www.ijcnn2013.org/ and http://www.icann2013.org/ http://delab.csd.auth.gr/eann2013/about%20eann%202013.php You could also search on google scholar for recent articles to get some pdf.

This will give you some ideas about what is the current popular topics related to neural networks. Then you need to find one that you like.

If you have chosen your supervisor already, I think that you should also discuss that with your supervisor.

Hi Jyoti I am interested in this topic Maybe we can help each other [email protected]

hello sir ,

Am willing to do research in data mining, can you suggest a good topics in data mining

I have answered this question in the blog post at the top of this page.

sir i m interested in data mining .but i feel troublin choosing my research topic kindly suggest me topik i shal b grate ful to u thanks

You should read the blog post on top of this page. It explains how to find a topic.

my area of interest is medical data mining can you please suggest some topic on it ?

No. I don’t work on medical data mining.

I am looking for a research topic within data mining in Big data. Please suggest me a topic and where to start looking for it ?

Thanks & Best Regards, arvind

Did you read the blog post?

sir, i would like to work on the concpt of audio and video streaming in dataminig. which topic will suitable for me for resarch. Please suggest the topic,

I’m not familiar with the topic of audio and video streams. You would need to do some literature review to know what people have done on this topic.

“Philippe Fournier-Viger”u r saying iam not familiar in this and that plzz mention in post in which area you are familiar

If you want to know about my areas of research you can have a look at my webpage: http://www.philippe-fournier-viger.com

Sir, iam seraching for a thesis topic in Data Mining for my M.Tech course. I select Cloud Mining but in this topic what i have to do i dont know. Please suggest me.

Thanks & Regards

In cloud mining, you could write some parallel data mining algorithms that can run on the Map Reduce framework. This is just an idea. There is certainly many other topics on cloud mining.

Thanks for the good articles. I want to do research on Data Mining Applications to Agricultural Field topic( I feel this interesting topic as of now). What would be the best topics on this. How to approach to these? Is that could be a good idea or not?

Yes, i think it can be good idea. I’m not familiar with agricultural field so I cannot tell you what are the best topics related to this. But you can find this out by doing a literature review. What you could do is to find a problem that people in the agricultural field have and then to see what data mining technique can be applied and what are the limitations. Then, if you could extend the data mining technique to address the limitation, it would be great.

Thanks Philippe for quick reply. I am reading all your articles and will follow them. Mean while understand the data mining algorithms using your suggestions(books,from open source software, etc). Try to implement small algorithms and get confidence…. Will contact you If I need a suggestions. Thanks

Hello Sir, I am Student of M Tech(CSE) and i want to do research on data mining and mine area of Interest in “Web Usage Mining and Social Network Mining”.Can u suggest me Certain topic regarding this Mining Area……

It is a good area, especially social network mining, because it is popular, right now. But to find a specific topic in this area, I cannot help you. You need to do a literature review.

Hello sir can I get ur mail I’d . sir I want to discuss in this

Hello sir, I like to do research in data mining sir but I do no what to do? And how to do? Please help me.. my guide is asking me research topic. I do no anything.. pls help me to find good topic sir in data mining or please tel me any nice topic. I do no what current process is going on data mining sir…

Its very ugent sir please help me pls..

thank u sir…

If you had read the blog post, I wrote that the question “please give me a topic in data mining” is too general. You should first learn about what is data mining and what are your interests in data mining because data mining is a very broad field. If you just ask for a topic in data mining, then I could suggest you any random topic such as: improving the memory efficiency of frequent subgraph mining algorithms for the case of uncertain data with applications in social networks. But will you like it? I think that it is better that yo u find a topic that you like. In the blog post, I gave the information about how to find a good topic.

Thanks sir,

And suppose i am selecting the Social Network Mining as a My Research Topic can u tell me for the Implementation purpose which Programming lang. or the tool is required? is there any specific Web Site from which i can get the Source Code

I do not work on social network analysis so I cannot help you too much. I know that some social network datasets are available on the web such as the DBLP dataset. There is some datasets here for example: http://snap.stanford.edu/data/ You can also find more by searching on Google.

I know that there is a conference called ASONAM about social network mining. You could check what is published there to get some ideas of what researchers do on this topic and there is some journals about social network mining such as this : http://www.journals.elsevier.com/social-networks/

For the programming language, I think that you can use anything you like. However speed is sometimes important in data mining. So I would not choose a language like PHP that is considered slow. Personally, I would use a language like C++, Java of C#, because they have good performance. But it may depends on what you do…

For source code, I don’t know. You would need to search.

hello sir.,

In ‘improving the memory efficiency of frequent subgraph mining algorithms for the case of uncertain data with applications in social networks’ its already researched sir you can see in this website… http://dl.acm.org/citation.cfm?id=1646028

thany u…

It is not a problem if someone has already worked on a topic. You can do something differently or better.

By the way, I just gave this topic as an example.

Very helpful post. I’ve somehow managed to choose a topic but I don’t know weather it’s a good topic to continue on my Ph.D or not. I’m masters student and I’m working on “online microbloging/social networks’ stream mining”. I want to know your opinion on this topic as an expert in data mining domain.

I think that it is a good topic. Social network and micro-blogging are popular research areas, right now, and it is good to choose something that is popular. Stream mining is also an interesting topic because it is more difficult than mining a static database.

Hi Philippe , A very Big thank for this article , It helps me lot . I m interested in “using association rules to classify medical data” , will you please give me more specific idea , in which way I could do research for this topic . how can I improve already exist research work in this topic.

I think it can be a good topic. In my opinion, for this kind of topic, you can have two contributions: (1) a contribution is mining the medical data to do something useful and (2) the second contribution is maybe improve some association rule mining algorithm so that it work better with your data.

It would be important to find some medical dataset for your project. Would it be patient record? datasets about drugs ? datasets about genetic data? datasets about diseases? etc. I don’t know. You would need to search for some data. You could have a look at what is available on internet. Or you could also see with your supervisor if you can obtain some medical data at your university if there is a medicine department. Ideally, you could collaborate with some people working in the medicine department that could help you about understanding medical data in your project and tell you what is important..

After you got your data and you know what is the goal that you want to do with your data, you could apply association rule mining and see what are the problems that you have with the current algorithms. Then, you could find some way to improve them so that they better suit your data and what you want to do with the data. For example, maybe you find that current algorithm cannot do X. Then you find a way to modify it to do X, whatever X is. Or if you find that association rule is not the best solution, then you can use something else.

For improving algorithms, you would need to understand the current algorithm and see what they cannot do for your medical data.

Thanks Philippe

hello sir can u suggest me some topic regarding “Frequent Pattern Mining”

I like to do research in healthcare using data mining.

please give me more specific idea about this topic.

You can make two kinds of contributions: – contribution about doing news things with the healthcare data by applying data mining algorithms – creating some new data mining algorithms or techniques that are better than existing techniques or deal with specific problems in healthcare data.

For more specific topics, I cannot help you because i’m not a specialist in healthcare. Furthermore, I gave the steps in the blog post about how to search for a good thesis topic in data mining.

hello sir, i am searching for the m.phil thesis topic in datamining and give some idea about the thesis paper.

You could start by reading the blog post above. In the last paragraph, I mention that the question “give me a topic in data mining” is too general.

sir i want to do research in data mining in the field of clustering please mention any new suggestions on this topic

Clustering is good. But you need to find something more specific. For example: improve the speed or memory usage of some clustering algorithms? modify the algorithms to handle new types of data? use the algorithms to perform something better? propose some new ways to detect outliers in clusters? …. I just say that as examples. You would need to do a literature review to know what is a good topic and what has been done in clustering (I don’t work in this sub area of data mining).

Sir, i want to prepare a journal on data mining.its a part of our syllabus.so could you please suggest one topic on data mining. take it urgent. Hoping for your reply at the earliest Thanku

Respected sir, I am Mtech student and m going for resarch in data mining…my guide says for selecting a particular field like data mining or cloud mining .sir plz suggest me which is best..

Data mining or cloud computing? I think that both are ok and popular in the industry. So to choose one depends on what you like.

sir if i select one from both data mining and cloud mining plz give your idea…….

You could design a new data mining algorithms that can run on the Hadoop technology for cloud computing. It could be a clustering algorithm, a pattern mining algorithm, a classification algorithm… or something else. Then when you design an algorithm, you need to show that it is faster, more memory efficient or that it performs new things that no other algorithm can do, or be used to do something new. If you do a literature review, you should be able to find a good topic.

sir what can i say to guide data mining or cloud mining………

As I said in the blog post, you should do a literature review so see what has been done and what can be done. This, you need to do it by yourself.

hi what about opinion mining? is it good topic for master thesis?

It is a good topic. I think that there should be many interesting challenges related to opinion mining. It could be challenges specific to a language (e.g. French, Spanish, etc.) or a specific type of opinions (political, etc.), or some technical challenges (improving the accuracy), etc.

thanks. is there a software for opinion mining? for example i know clementaine for data mining.

Maybe. But I don’t work on this topic so I’m not aware of this.

what about data mining in cancer, diabets and heart disease data? are they good? or they are old?

There are certainly some things to do on these topics (you would need to do a literature review to see what has been done recently). For medical topic,s a challenge is to obtain medical data and find a specialist that could guide you about what the data means and what would be important to do with this data. If you can have access to medical data (maybe that there are some on the web too), it could be great.

sir i want to start mtech thesis in cloud mining or web mining sir plz suggest me any suggestion………..

I suggest to read articles from recent conferences on these topics to see what has been done recently. This will help you to choose a topic.

Hello sir, the subject of my thesis is the prediction of links in social networks. What is your opinion and do you can help me with some ideas.

I don’t know much about social network mining. If you want to know what is the latest research on this topic, you would need to do a literature review (search for articles on this topic in recent conferences/journals).

first of all i would like to give my thanks for the article. It is really vry helpful for new researchers . I want to do Phd in “Data Mining on e-learning ” plz suggest me some research topic on it.

You should look at recent conferences about data mining & e-learning to see what are the current topics and see if there is something that you like. For example: EDM, AIED, ITS, ICALT, EC-TEL etc.

Thanks for excellent support to researchers, my topic is privacy preservation in data mining. Is topic is good to work on? can u pls. suggest some problems on this topic. I am interested in consumer behavior and dental field. thanks in advance..

Privacy preservation is definitely an important topic, since more and more data are collected about individuals or other sensitive topics and it is important to protect it. For the challenges, and finding a good specific topic, the only way to find out is to do a literature review (read recent recent articles on privacy preservation to see what other researchers have done on this topic recently). Me, I don’t work on this topic, so I don’t know what is the latest research on this topic.

Thanks for your help sir..

Its great that science doesn’t require any boundary..

Thanks for your expertise.

You are welcome! 😉

You could: (1) improve association rule mining algorithms (2) apply them in novel ways on medical data or to perform something better by using association rules (3) do both (1) and (2).

But you need to define more precisely what you want to do with medical data and what kind of medical data. Personally, I have no knowledge about medical data. The only way to find out is to read articles on this topic or to find some expert from the medical domain who could guide you about what is challenging in the medical domain. Also, maybe you can check if someone has done a similar topic before and identifies what are the limitations of their work. This could be a starting point.

You could also start by searching for medical data because you need data if you want to do data mining! Depending on the data that you can get and what you want to do with the data, it may gives you some hint about what are the problems with the current association rule mining algorithm for this task and what could be improved.

Hello sir, I like to do research in data mining sir but I do no what to do? And how to do? Please help me..

As I said in the blog post, data mining is a very broad field. If you don’t know what you like in data mining, then perhaps that you need to read about data mining first (e.g. an introductory book), or read articles from recent data mining conferences and see if there is a topic that you like.

sir i have to choose a topic in data mining for thesis in mtech having to read this blog i got topic which i like are using association rules to classify medical data so suggest me how initiate work on this

I have answered this question already. You can read my answer, two messages above this one.

As I said in the blog post, I will not answer the general question of “give me a topic in data mining”, as data mining is a very broad field and I will not do a literature review for you to find a good up-to-date topic. I suggest first reading about what is data mining and then to look at recent papers published in data mining conferences and choose something that look interesting .

Hi If I want to do data mining using fuzzy logic, can you help me

You need to define your topic more clearly. Fuzzy logic is a technique for representing fuzzy things. But then, what you want to do with fuzzy logic? You want to apply this to what kind of data? Why you want to apply fuzzy logic to this data? What is your goal?

sir i m doing research in cloud plz suggest me any area or artical or topic.

You need to read articles from recent conferences to see what people have done on this topic recently, as I explained in the blog post. This will give you ideas about what is interesting for this topic.

Hi I have great interest in data mining. but my professor field is not data mining. but I want work on data mining. Will you please help me in this regard. or suggest me person that work on data mining. I’ll give you the final results. I hope you can help me

Maybe try to find another professor at your university that is working on data mining?

hi i am M.tech student i want to do dissertation in data mining my interest to do research in security in cloud computing with data mining can you pls suggest better topic or any idea about data mining in cloud computing pls give me quick reply its very urgent!!!!!!! pls…….. thanx

As I said in the blog post, I suggest reading recent articles related to your topic in recent data mining conferences/journals to see what is popular right now and find a topic that you like.

Hi ankita , I see that you post this almost a year ago and i have the same interests in my research. So how was it!? and what suggestions you may kindly offer to enhance my proposal.

many thanks dear. Firas M.A

Hi Philippe,

Fantastic website, Recently I’ve started my 4th year of college and have chosen data mining as an elective. Im looking into the major challenges faces in adopting data mining techniques within the public and private sectors. Just wondering could you steer me in the right direction.

Many thanks, Colm

Thanks. I’m glad that you appreciate the website.

I’m into the technical aspect of data mining. I’m a professor in Computer Science. What I do is that I design some data mining algorithms and we apply them in some research projects. I don’t work closely with the public/private sectors since I’m working on the fundamental aspects of data mining (algorithms). So I cannot give you a lot of information about the challenges for adoption of data mining techniques.

Some challenges that come to my mind are: – you need some trained people to understand what is data mining and how to analyze the data (e.g. data scientist) – data mining techniques offered in data mining software are not always well-suited to all domains. All data mining software offers a limited choice of data mining techniques. If they don’t fit with what the company wants then you perhaps need to hire some data mining specialist to design something custom. -…. Philippe

Fantastic, That’s a great start none the less. I appreciate your reply. Thank you again.

sir m doin a research on Research issues on web data mining is this a good topic… i have read the introductory part nd some pdf also what else i can do in that in order to make my research better.

Yes, web data mining should be a good topic. Then, I think that you need to continue reading on this topic. By reading on the topic, you will see what people have done recently and get some ideas about what you can improve or do differently. It is important to know what other people have done before starting to do a research project.

I’m MSc student interested in data mining for my thesis. I have got a good approach how to do my thesis. Your suggestion is valuable. My thematic area is weather forecasting, what do you help me in identifying specific title. It is urgent! Thank you very much

This is great information, thanks’ for share!

Dear Sir/Madam

I am pursuing M.Tech(CSE), I want do project in data mining, and also same project need to PhD thesis after MTech. Shall you give me good research topic on data mining? Please reply me Thank you

You can read the last paragraph of the blog post starting with “Lastly…”. It answers this question.

i need to make my thesis in data mining using cluster algorithm for web pages to make semantic web

I have choosen mood identification from music data as my topic.its a part of multimedia data mining.so i want to research using data mining algorithm.so can you give me suggestion on this topic.

thank you sir.

sir, i want to do research in data mining for my m.phil. how can i find a topic for my research or give some topics for suggesstion. or any website/book i can refer pls give that address/bookname

thnks in advance

You can start by reading the blog post.

hi sir, i want to do my mtech theisis on clustering or classification. can u suggest any new topic in this field???????????? thank you sir

your type of article should be encouraged in research development. the article has directed my path on how I can come up with a researchable topics. I will still call on you after following your laid down approach to data mining. thank you Sir

Glad that you like the article and that it is helpful.

I want to do my PhD work for privacy preserving in data mining for various transactions.Suppose I purchased items among a b c d e f g h 1 0 1 0 1 1 01 . 1 means I purchased that particular item and 0 means not purchased.Which kind of technique I can apply in to protect binary data.I think the Privacy can be protected by using some cryptography algorithm.I am little bit confused how I can implement my idea with these binary transactions. please help.

Yes, I think that you can encrypt your data using an encryption algorithm.

The fact that the transaction is binary or text should not make any problem for an encryption algorithm. In any case, you will give data to the algorithm who will generate encrypted data.

To know how to do it in a particular language such as Java, etc. you would need to see what are the encryption algorithms available and search for some tutorials, I think.

i m doing thesis in cloud mining secuirty,sir plz suggest me any important topic………

I think that you need to define your topic more clearly. Cloud mining is a too broad topic. You need to make a literature review to see what is interesting in this area or discuss with your research advisor. Me, I don’t work in this area of data mining and I will not do the literature review for you 😉

Hello. I’m a Business Analytics (M.Sc.) student. I would like to write my thesis about a specific problem on business data which can be solved by a data mining algorithm. I like the kind of problems from the Data Mining Cup. I can not decide on any specific algorithm right now but I know that the problem should be business oriented. I would be thankful for any advice.

sir i am an assistant professor in comp. aplications. i am interested to do a research in agricultural field especially in cardamom cultivation. it would be helpful if you suggest some related topics to the starting of the work

Hi Your opinion do we work on the subject ‘ data mining based constraint’

What is “data mining based constraint”? Data mining with constraints? This seems too broad. I think you need to define topic more clearly.

Sir, I m interested in “using association rules to classify medical data”, but i am not so good in programming.Is it possible for me to continue this topics without programming or is the source code available in the web site? if source code is needed , will you able to provide me some links for source code?

There are some source code for association rule mining such as my own data mining library: http://www.philippe-fournier-viger.com/spmf/

However, if you want to make a Ph.D. in Computer Science you will most likely need to do programming because doing a Ph.D. or Master degree in Computer Science without doing programming does not make sense, I think.

Another possibility is to do a thesis from a mathematic perspective if you don’t want to do programming.

hello sir special thanks for the article. sorry if it is also includes in the “can’t be answered ” questions. I would like to do my Ms thesis regarding Social network analysis, I do some research but I can’t decide yet ,which filed: could you suggest me some more conference or journals regarding this topic despite from http://asonam.cpsc.ucalgary.ca/ and http://www.journals.elsevier.com/social-networks/ which you already mentioned or any other way to extract open areas in this field.

thank you so much regards,

I don’t work on this topic so I cannot help you much. To answer this question, I would need to search just like you would do.

Sir, i have to work on “finding closed frequent patterns and their association rules” on data mining, i’ve tried but cant find better approach to work with, so please tell me some parameters and approaches that can improve the efficiency. thanks

If I had a very good idea about this I would keep it for myself as I also work on pattern mining 😉 There are certainly some ideas but it takes time to find a good idea and when you got a good idea is better to keep it for your own research. If you cannot find one, then you can consider variations of the problem of association rule or itemset mining. For exampple, if you consider uncertain itemsets, fuzzy association rules, etc. etc., then maybe these topics are less explored and it is easier to find some ideas. This is just some thoughts. In my opinion, it is easier to create a new problem or a new algorithm for a variations of the problem of association rule mining than to try to make something faster than the best algorithms for association rule mining.

can you please tell me few research topics on fuzzy multimedia data mining related topics

Hello Sir, I’m interested in Big data. 1. How does data mining and Big data are related ? 2. I scanned the internet for Big data architecture, i cant get clear picture of it. Can u suggest me a standard article or book for understanding Big data ? 3. how to develop a own architecture for large data handling ?

Sir, can you please tell me the research topics in data mining for m.tech thesis. i am not good at programming.

Did you read the blog post? It explains how to search for a topic.

Sir,I need some topics which have less programming to do as a project.please suggest me some ideas.

Please read the blog post carefully. It explains how to search for a topic.

sir! give me some idea that what new can i do for “feature subset selection” in data mining….

I don’t work on this topic.

Sir, Im dng Mtech my research area is datacube Materialization & MapReducing.. can you gve some suggessions for me..

I would simply suggest to apply the advices that I gave in the blog post.

Really good. Many thanks to Mr.Philippe for his replies.

i am searching Ph.D research topic in privacy preserving data mining

Did you found your topic

Sir, May I get data set for doing my research on “data mining for elearning” pl repy.

You may find some by searching on the web or you may record your own data if you have some e-learning system at your university. There also exist some educational data repository like PSLC datashop: https://pslcdatashop.web.cmu.edu/ But personnally I would prefer to generate my own data than to use the data from someone else.

Sir, I am doing M.Tech in Computer science and technology. i want to do final project on data mining but i am unable to find any problem in data mining so please tell me the problems on data mining or any other areas. please take it urgent. I look forward for hearing good response. Thank you. Neeta Jadhav

This blog post explains how to choose a topic in data mining. That is the point of this blog post. Have you read it?

sir, I m student of Mtech and I am not able to find topic for my thesis can you suggest me some interesting topics.

The whole point of this blog post is to explain how to search for a topic. I suggest to read it. Moreover, as I wrote in the blog post, I will not answer this question.

Sir, I am seraching for a thesis topic in Data Mining for my M.Tech course.

can you suggest me some recent topics, I m not able to find any topic. just suggest me some topic so that I can start reading about it.

memory efficiency of clustering algorithms, is this topic is good.

I think it can be a good topic.

But you would still need to search a little bit to see what has been done already.

I am doing an undergrad research on multimedia mining can you suggest a topic?

No sorry. Looking for a topic takes time. You need to do a literature review to see what has been done before. I cannot do this for you.

Hello sir, I’m doing my thesis on closed frequent patterns and association rules, i am trying to find it by combining “n-list” and “charm” , So, how can i use any frequent pattern finding structure to find closed patterns, or is it possible. Any guidance plz….. Thanks,

There are many ways to solve the problem of closed itemset mining. Several algorithms have been published. To my knowledge, LCM should be one of the fastest according to the last FIMI competition in 2004. Other fast algorithms are DCI_Closed, FPClose, Charm etc. So, yes there are certainly still possibilities to improve these algorithms.

How to do it ? I would say to read recent articles to see advances or ideas in that could be transposed to closed itemset mining. Or to try to find new ideas. However many research have been done on this topic. To make a significant contribution on this topic one would preferably need to compare his new algorithm with the fastest closed itemset mining algorithm.

About nList, I have briefly read about it and it seems to be another variation of the FP-Tree. Therefore, perhaps that it would be easier to use it with FPClose than Charm…. I don’t know. Besides, I have noticed that the author of nlist seems to be posting links to his own article on many internet forums including on my own forum several times. I had checked the paper but I was personally not convinced by the experiments.

sir, i want to work on data mining in cloud computing? can u suggest me any appropriate topics related to this field. balvinder taneja

I would suggest to apply the recommendations that I wrote in the blog post on top of this page.

Sir, I am seraching for a thesis topic for my M.Tech course. can you suggest me some recent topics, I m not able to find any topic. just suggest me some topic so that I can start reading about it. n my area of interest is network security or data mining

As I said in the blog post, I will not suggest any topics. Looking for a topic takes time and I don’t have time to search for topics for other people. Moreover, as I explained in the blog post, you should choose a topic that you like, not a topic that someone else has chosen for you.

If you have no idea what to do about data mining and network security, then a good start is to search for papers about data mining and network security in Google Scholar to see what other people have been doing on this topic.

Really nice article , I just want some research topics using clustering

Hello Sir, I am very glad to know that someone is guiding the humanity in some way, like you through such a honestly interesting page. Sir i am searching for my thesis topic ……..but after studing your kind blog and following your steps I am going to do my research in obtaining a data about earth properties and their environment and materials condition and based on that data i am going to know that to some extent that in this place there is occuring that ratio of iron , gold water etc.. Is this will be good for me . Need your kind reply. thanks in advance.

Hello, Thanks. Seems like a good topic to me. But you would still need to see what other people have done before to see what you can do differently or improve.

thanks sir plz help me in this i will inform you daily…

sir, what possible topics can i do if i like to work on text mining i want to do a research that can be use to our school can you suggest some ideas ?

plz reply thanks mr.phillipe

As explained in the blog post, to find a good topic, you should do a literature review. It takes time and I cannot do this for you.

I want researches about applying Data Mining techniques on master and PHD thesis and dissertations database. Thanks.

You can use tools like Google Scholar to search for articles.

Sir i want to do my mtech thesis on page ranking algorithm in data mining. Please suggest me some good topic in this for my thesis.

The blog post explains how to search for a topic. Did you read it ?

Firstly, I would thank you for useful article, then I am interested in clustering techniques for Data mining. Please guide me a topic for my PhD dissertation. My email is [email protected] thank you.

Hi, As explained in the blog post, I would suggest you to do a literature review on this topic to find out what has been done recently on clustering. You can start by searching on Google Scholar to find some recent articles on clustering and to see what are the current challenges.

Best regards,

I am doing my research work in recommender systems (Data mining area) and concentrating attack concept. Now i am seeking classification, clustering algorithm. which one is the best and how to proceed?

I have no idea what is “concentrating attack”. I think it depends on what you want to do. Classification is generally to classify instances in a set of predefined categories. Clustering is generally to automatically create categories. So they don’t exactly do the same thing.

i have decided to do my thesis on focused web crawlers for gathering educational material on the web. i just wanted to know if this topic is relevant in today’s time , i hope it not a very old topic?

I think it is ok. It depends on how you do it. You need to read articles on this topic to see what has been done already on this topic. I personally don’t know what has been done on this topic. Depending on what has been done, to make a good research work you need to improve or bring some new ideas. If you can do that on that topic, than the topic should be good.

Hi, I am looking for a research topic within data mining. I am interested in multimedia data mining in social media with social event detection as an application. My problem is to find a gap and a topic for my thesis. Please suggest me a topic and where to start looking for it? Best Regards, Sam

As I said in the blog post, looking for a topic takes time. You need to look for it by yourself or ask help from your research advisor. If you have no idea where to start, then I suggest to just search the keywords that you know in Google Scholar to find some articles or look for some general survey articles perhaps to get an idea about the whole domain first. Then when you find something interesting, look deeper.

Hi I am a Phd student of medical datamining but Im still trying to identify my research questions … I am looking on applying datamining algorithms on clinical datasets as i dont have strong programming skills…could you please suggest any new applicable methods that is yet to be explored as my possible reseach questions?

Thanks, Hope to hear from u soonest!

I don’t have time to search for you.

hello I like to do datamining on blood transfusion data plz guide me how I can define subject about it

Hi Philippe

It is truly astonishing to see how many ‘graduate’ students completely misread your post! I stumbled on this post during my own search for masters ideas, and I guess I got my night’s entertainment too.

Good post, and I applaud your patience.

Hi Francois,

Thanks. 🙂 I now have the proof that someone has read the whole blog post 😉

I read your blog too and I also translate and put it on my website to help thousands of students who are concerning about their topics!

Great job. Thank you very much!

That is great. If you translate the content word by word, then please cite the original website as the source of the text.

Hi, thanks about your recommendations they are so help full. can you plz recommend some topics about outliers in data mining. I read all of the comments but i could not find any related things . is it some topic that common or it was old and have not any new thing for research. thanks alote philip.

If a research area become less popular, it does not mean that there is nothing interesting to do. There are always some research problems. You just need to find one.

For specific topics, I recommend to follow the steps in the blog post, which starts by doing a litterature review.

hi sir can you please tell me related topics on Analytical hierarchy process in data mining for Ph.D. Please give me more specific idea about this topic. Plz reply s soon as possible

Hello, I don’t work on this topic. You would need to do a litterature review as explained in the blog post.

Hello sir, I have to give a research proposal for M.phil in computer science. can you please suggest me some brief review type of research topic in neural networks(extensible).

No. I don’t have time to do it.

Sir i want to relate data mining in agriculture.. but i’m unable to find any specific thing. plz guide me for my PhD topic selection

As said in the blog post, you need to do a literature review and/or ask your supervisor to help you.

I want to do my research in data mining…

please sir can I apply any data mining technique on DIP..

or to develop any data mining technique for image processing…

I don’t work on image processing but there are certainly some way to apply data mining on images.

For example, there are some works on spatial association rule mining to find patterns in geographical data. This geographical data could be extracted from satellite image before applying the data mining algorithm. This is just an example. You would need to search to find more information about the possibilities.

data mining have different technique like svm ,ANN , PCA, decision Tree ,rough set theory, clustering that are mostly used for DIP

Hi If i can use current method of text mining for another language, for example English method for Arabic, would it be a new challenge for my thesis and write a new article? Arabic language is different from English, so by using current method in different langue i can achieve new result.

From a data mining perspective, it is a new challenge, if the method do not work well with Arabic and you need to modify the method to make it work for Arabic. If the method just work for arabic without modifying it, it would seem too simple, perhaps. But text mining is not my specialty. You may have a look at papers about text mining for other languages to see how they have presented their work.

Hi It is about a year that a write a comment here :). On that time I choose opinion mining as my thesis topic. I work hard to fine a new approach in opinion mining. But you know, I was not so successful in this way. I make a new sentiwordnet for a new language from existing wordnet. but I couldn’t suggest a new method. some one told me, that by combing existing method I can introduce new one. but how can i do that ? for example by combing svm and naive bayes how can i create a new method? really I need your help. how can I continue my thesis in a way to find a new idea? thanks

hi hello , im student of MTECH 1st year and want to do reseach in data mining , by which i can easily take admission in phd programe .. so tell me topic which have wide scope of research ?

I really need more help into the research of mining and what questions I should research more on it is due tomorrow and I do not know anything

I need help to answer the questions I am a year 12 pa student from international overseas

Sir, can you please help me, i am searching for PHD research topic, i am thinking for context aware computing in mobile computing.is it a good topic to move on?

I don’t work on this topic. It sounds ok but you need to do a literature review and maybe make it more specific.

hi,i need help i must impelementation this paper with c# but i dont know how do ?plz help me

A Novel Method for Privacy Preserving in Association Rule Mining Based on Genetic Algorithms Mohammad Naderi Dehkordi Ph.D Student, Science & Research Branch, Islamic Azad University (IAU) Department of Computer Engineering, Tehran, Iran

I cannot help you.

Yes good article i really like it i will find out how we can improve better library services though data mining i will also try to learn it

Any management related data mining topic for MSc thesis? I don’t have much algorithm related knowledge , got programming knowledge in c/c++/sql.Please provide me some recent topic relatd to management related .

To find a recent topic, you need to do a litterature review or talk to your supervisor. There is no way around it.

dear sir i am too much confused that what topic for thesis should i select. if you have any good topic so plz guide me thanx .

if i want to do thesis in datamining first thing i need is the data sampel. but in our country people donot give any data.. then can you give me solution for findind the data samples for datamining thesis.

Then, you may have to collect your own data or to use public data from another country if there is some available. There are many websites that you can find using Google that offers public datasets that you can download. If you are lucky, there will be some public datasets.

Otherwise, depending on your topic, you could consider generating some artificial datasets but it may not be a good idea depending on your project. In general, it is recommended to use public data.

can you say me how can i collect the data sets required for datamining because it is impossible in the country like nepal..

How to collect the data depends on what kind of data you want to collect.

For example, if you are doing data mining on network data, then if you have access to a webserver or router, you could just use the logs from the server or router to perform data mining. Another example is if you are doing data mining on source code from software. You could just download any open-source project and perform data mining on the source code.

On the other hand, for some types of data, it may be very difficult to collect data. For example, if you want to do data mining on medical records, it may be very difficult to get access to that kind of data from the government, unless some public data is already available.

it was very usefull article. thanks so much:-)

Thanks yr bog provides very important guidelines. sir I want to do Ph D in Data Mining. I read yr blog & from that i decided to do work in Data Mining on Medical Data(gynec or orthopedic). I can get database of patient, diseases etc as one of the my family members is a doctor. so pls could you tell me what can i do after getting a database.

It is good that you have data, but what is your goal ? what do you want to do with the data? Do you want to perform early prediction of who will get a particular disease? do you want to predict what will be the reactioon of a patient to some drug prescription? etc. You need to decide what you will do with the data. To do that, you should have a look at what other people have done by reading some articles on this topic.

Thanks for reply Sir i wanted to predict the baby born date and prediction of premature delivery. so can do , how to do

You would need to have some training data. Then you could train a neural network to do the task of prediction. Similar techniques to neural networks may also be used like SVM…

after selecting data how to select algorithm or method

In general, it is recommended to try the popular methods first. To know what is the popular methods for your topic, sometimes it is necessary to read articles on the topic. In your case, you could try neural networks directly I think. If it does not work well, then you would need to find something else by reading. Or sometimes in data mining, we even need to modify the algorithms to achieve what we want if there is no algorithms that are appropriate for what we want to do.

Hello sir, I am pursuing mtech. I have chosen opinion mining under data mining. Can you suggest some topics in it? Can you suggest which tool I should learn? I am free for 2 months. I want to utilize this free time in doing something productive in topic. So can u please help me ?? Thank you

As said in the blog post, you should do a litterature review, which means to read articles on your topic (opinion mining). This will allows you to find some topics. I cannot do that for you.

hello sir, i am willing to do research on data mining field, i have decided some topics like data mining techiniqs for online social networks and analysis, web mining for predicting user behaviours , topic detection and tracking .. out of them which one is suitable or how can i combine all these issues..

These topics are are quite general, so any of them would be ok. But in any case, you will need to define your project more precisely. And to do that, the only way is by reading papers on these topics and try to get some new ideas about what could be done better or differently, or what has not been done yet. To do this takes time, and you need to do it by yourself or with your the help of your supervisor.

I will be try for Phd in Psychological data & Data mining concept but my work that analysis of mind traffic. Psychological or spiritual concept & data mining Concept combined for research work &Is it contribution of Computer Engineering? suppose yes then please tell me right way……………….

Yes, it can be. It is definitely possible and interesting to combine Computer Science with other disciplines such as Psychology. Now, whether it deserves a Ph.D in computer engineering depends on whether you will be solely a user of the software of someone else or you will develop your own software program that solves non trivial computer science problems with respect to your application. You may also want to check previous thesis published at your university to see examples of Ph.D thesis subjects that have been accepted as PhD thesis.

Thanks for guideline…………….

dear sir i am completed mphil too much confused that what topic for thesis should i select. if you have any good topic so plz guide me thanx .

As I said in the blog post, I will not provide topics. But I explain how to search for a topic.

sir i am interested in data mining in educational or learning field for my m.tech dissertation can u give me some specific ideas abt it???

Data mining in educational or learning field is still a very broad topic. You need to define something more precise.

You could search papers in Educational Data Mining (EDM) and Learning Analytics communities (LAK) for papers on these topics. Or also in some e-learning related journals and conferences.

sir for decision support in dental disease prediction which kind of further research i can do for masters can u give me some idea about this ?

i need your guidence .. to choose my thesis topic on data mining

The blog post on this page explains how to choose a topic.

Sir, I was choose datamining for my phd. 2 years over.but still i did not choose proble. but my dataset is medical imbalanced liver data dataset. It’s correct or not .how to choose problem based on this things and how. please help me.

I think you should discuss with your research advisor to ask for some help in choosing your problem. I cannot give you any ideas since I don’t work on medical data and choosing a problem takes time. Actually, what you probably need is a medical expert that can tell you what are the important problems in the medical field that you could solve. Otherwise, you could have a look at related papers and see what other people have published on this type of data.

Sir, I was choose datamining for my phd. 2 years over.but still i did not choose proble. but., my minor project topic is TECHNOLOGY AND ITS IMPACT IN THE CLASSROOM. i think continue this topic . pls help related to data mining or big data. It’s correct or not .how to choose problem based on this things and how. please help me.

another question big data is new one. to choose related to the topic in TECHNOLOGY AND ITS IMPACT IN THE CLASSROOM. pls help

Hi, technology & impact in the classroom looks like an important topic for the society.

If you want to do data mining related to this, as I have described in this blog post, you will need to read research papers on this topic and to find something that has not been done before or that you think you can do better. I cannot help you to do this because it takes time to read papers. But you can ask your research advisor for help.

Another important point in data mining is that you will need data for your research, either by downloading it or by collecting your own data. Since the availability of data may have an influence on what you can do for your topic, you could also search on the web or ask authors of papers if they can provide their data, if you plan to use the data of someone else rather than collecting your own data.

Sir, i would like to do a Masters dissertation on the topic data mining in Automotive Diagnostics. Could you please suggest me some topics

For data mining, you need some data to do the mining. Can you obtain a database of automative diagnostics. If yes, then you could try to build a system that may automatically diagnose what is the problem of a car based on what he learned from the database. That is just an idea. I don’t work on this topic and I did not do a literature review. So it does not mean it is a good idea. As I said in the blog post, what you should do first is a literature review.

Sir I want to do Ph.d in Database. I have knowledge of Batch programming. But i am unable to find that how can i choose topic for research and how i use database and batch programming combination in research area?

Please help me.

In my opinion, if you want to do a Ph.D in computer science, you need to know some programming language like C++ or Java. If you only know batch programming (such as .bat files), it seems hard to do a Ph.D. in computer science.

Sir i have good knowledge of database also. i have done my internship in oracle. and i am very interested to do ph.d in database. but i am confused to choose a topic for research.

As explained in the blog post on top of this page, choosing a topic takes time, and you need to do it by yourself or with the help of your PhD supervisor. Basically, it requires to read recent papers to find what is popular topics now and think about some ideas or something that has not been done.

Sir can i choose “Data Mining Automation Process” topic for research area in Ph.d?

Helo sir, now i’m doing my m.phil computer scince, please give idea to select the topic in data mining area,

You can start by reading the blog post on top of this page. It gives you the steps to find a good topic rather than giving you a topic because finding a topic takes time and thus I cannot do it for other people.

And also need some research paper using clustering, now I’m interested to do my project in mat lab so give topic to related to using mat lab

hello sir, i have chosen “stock price prediction using machine learning algorithms” as my project topic…and i got information from my senior that we need to consider the stocks of america or London as Indian stock market is not so strong and we can’t get much data..but i don’t know from where i can get stock data for mining..and how can i access data and make use of it for mining in my project…please guide me from where can i get stock details of these countries stock market and how to mine that data??

I have not worked with stock data before. But usually, here are the ways to get data for a data mining project: – Find some data that have been collected by other researchers . If you read some papers that have used stocked data, they may indiciate that the data is available on their webpage. In that case, you can download it directly. If its not, you may contact the authors of the paper directly by e-mail and ask them for their data. You may tell them that you will cite their paper if you use their data. – Collect your own data . You could write a small program such as a web crawler to browse some website that offers stock data and collect it by yourself. – Check some dataset repository. There also may be some webpages that are datasets repository such as the UCI repository that offer many datasets. You may check them. But I don’t think that there are stock data on UCI, for example.

If you collect your down data, the advantage is that you will have all the data that you want. But you will spend more time to collect the data.

If you use the dataset of someone else, then, you may not have all the information that you want, because the other researchers may just have collected the information that he needed for their research.

hi….. really good one. this page especially helped me a lot as i am in my initial days for selecting my research topics this helped me alot like how to start? where to start? i am really thankfull to the advisor.

You are welcome!

Dear Philippe Fournier-Viger I have real benefitted from your blog. Most appreciated, it has real enlightened me on choosing my MSc research proposal.

Nevertheless, how do i write aproposal to be approved by the panel. Kind regards, Thaddeus

I’m glad that the blog has been interesting for you. Thanks for your feedback!

Hi Philippe Fournier-Viger I am a beginner in data mining. As i am going to carried out my Ph.D in data mining applied in bioinformatics. I am really confused how to start my work.

I really thank prof.Philippe Fournier-Viger for replies and help .

sir i want to choose a research topic in data mining sir what can i do for choosing a research sir i need those topic which are so sample and are not complex sir plz say me today

hello sir, i did my M.Sc research on enhanced data mining algorithm on online Electronic shopping please i want to some topics related to this for my PhD research works.

I cannot search a topic for you because it takes time to find a good topic. But you may read the blog post on top of this page which explains how to search.

Hello sir, I need your guidance to choose topic in conferrence,So sir please give ideas about some recent problems in data mining, and how to solve it?

I only have time to search topics for my own students. The goal of this blog post is to explain how to search so that you can do it by yourself.

sir,my topic is “stock market prediction using machine learning algorithm”, first thanks for your idea regarding collecting data from the different sources, sir now i am planning to use any machine learning algorithm in prediction and not understanding among SVM,ANN and K-Nearest Neighbour techniques which will be more suitable for stock prediction?? please do help

hello sir, i have chosen “opinion mining” as my project topic, my question is what is new”hot” areas. i know some topic like: 1. opinion mining classification 2. opinion extraction 3. opinion search and retrieved 4. opinion spam detection 5. quality of reviews 6. lexicon generation i am interesting on recommendation system based on opinion mining . how about that and please suggest me new areas . my 2 ed q is how to find a real problem ???

Since I don’t work on opinion mining, I’m not aware of the recent topics in this area. You could find some ideas by reading the recent papers in conferences on data mining, AI or recommendation system (e.g. RecSys), or journals. I see that you have done some litterature review already. Then, you should continue reading 😉

thank you prof Philippe Fournier for your guidelines, my second question is how to find a real problem ?

I’m not sure what you mean by “real”. I guess that you mean important problem with real world applications. If this is what you mean, then well, there is different way to find interesting problems to solve. One way is to read papers and see what are the problem solved by other people. Then you may define the problem slightly differently than what they are doing. Or you can use their problem and propose a better solution to their problem. Or if you want to find some ideas of problems that are more applied, you may talk with expert in the industry, etc.

I really have to commend you Philippe for continuing to respond to comments over a year and a half later! This blog post was very helpful. I have already been reading quite a bit of literature and I am running into the problem of having too many interests. I just have to focus on one and go with it. Thanks!

Hi, Thanks. Glad it is helpful.

Hi I wrote a comment but there was no answer 🙂 I write it again. Hi It is about a year that a write a comment here :). On that time I choose opinion mining as my thesis topic. I work hard to fine a new approach in opinion mining. But you know, I was not so successful in this way. I make a new sentiwordnet for a new language from existing wordnet. but I couldn’t suggest a new method. some one told me, that by combing existing method I can introduce new one. but how can i do that ? for example by combing svm and naive bayes how can i create a new method? really I need your help. how can I continue my thesis in a way to find a new idea? thanks

Hi, sorry for not answering you the first time. I have been a little bit busy recently. First, thank you for coming back to this website! Doing research is hard… it is not always easy to find new ideas. That is true. Sometimes, it may takes times to find something interesting. Or sometimes, we may have a new idea but if may fails or provide bad results. This is normal. The more you do research, the easier it will get. Yes, it is possible to combine two methods to get a new method. But it should make sense to combine the two methods. We should not just combine two methods for combining two methods. For example, combining naive bayes and SVM may not make sense depending on what you are doing. Also, combining two methods does not mean that you will get better results. Moreover, if you combine two methods, but the combination is straightforward, then maybe that it is not a significant contribution because it may be too simple.

If you are working on a new language, then you may try to find what challenges are specific to this language. If you can identify some new challenges for your target language than you may find a way to adress these specific challenges and it could be your contribution.

I just talk in general because i don’t work on opinion mining. But don’t worry. If you work hard enough you should find some new ideas.

thanks. But sometimes I become hopeless. I think maybe opinion mining a research area that there I cant continue it because the are so many research about it and is not possible to find a new method.

kindly suggest me the topic for research on data mining so i can start my theses on data mining.

thanks & regards amit kumar

Hello Professor Philippe

I am Nishat Undergraduate Final Year Student. I would like to do my undergraduate thesis on Data Mining. I have read several article on it still I am little bit confused about it. However, I would like to do my thesis on Data Mining specifically on Social Networking site like – facebook or twitter. However, I need your suggestions how to start working on them. It would be really great if you could provide me some suggestions. I am looking forward to hear from you.

Hi, Thanks for commenting on this blog post. My suggestions: – read papers about social network mining to know what other researchers are doing. In your thesis, you need to do something that other people did not do yet or do something better (faster, more accurate) than what they are doing. So you need to know what they are doing. – you will also need to find data. Either you try to collect your own data. Then you should try to work with the API provided b;y twitter etc. to see what you can collect. or you can try to find some public datasets or contact authors of other papers to get their data. This s important because you need data to do your research and dependng on what your data contains you may or may not be able to do some research project related to social network mining. Best,

Dear Professor Philippe

Thank You for your Kind Suggestions.It shows me a way to start work on my thesis.

So to start with, im helping my friend to find good reference in finding the thesis topic. she kind interested in school related topic sir. can you suggest me anything or give me a little hint as milestone maybe, i would really appreciate it. thanks

Then she may like e-learning topics. If she is interested in data mining, she could check “educational data mining” and choose some specific topics in that area.

I am student in computer scince . I would like to do my undergraduate thesis on Data Mining , I want to use wireshark to collect data from intranet (small lan (univercity))and track the movement about ip and protocols and use data mining tools to know types of protocols and IPs and content of massages, I need your suggestions how to start working on them. It would be really great if you could provide me some suggestions. I am looking forward to hear from you.

Thanks for message. I cannot help you too much since i’m not working on this topic. But the most important in a data mining project is always to have data. You may try to collect some data to see the feasability. Besides, what is even most important is to read what other people have done about applying data mining or machine learning techniques to analyse network data. There are some people who have work on this topic before. You need to read at least briefly their paper to see what they have done. This is important because in research, it is expected that you will do something new that other people did not do, or something better than what other people did.

Hi, I have a data-set of 1000 feature. I use svm and naive bayes for classification. I use present-absent and TFIDF for them. but the result is different. how can I find the reason of this difference? I want to know why svm and NB got different result and why using different feature cause different too.

there are 1000 feature and I cant examine them one by one its correlation. is there a method for that?

Different techniques gives different results. Why is a difficult question to answer. You may need to make several experiments with your data to find out why. And as you said, with 1000 features, it may be complicated. But have you tried to use some techniques to reduce the number of dimensions like PCA? Or you could just do some pre-processing to remove some attributes and perform some tests to see the results. Just some ideas.

thanks, but by using PCA only the number of feature decreased, but I cant understand which features have more effect on the performance of classification. can you you guide me which experiment should I do to understand the reason?

Ok. If you have a target attribute X and you want to know which attributes A1, A2… Am is more important with respect to X, then what about just calculating the Pearson colleration between each attribute and X?

yes, but I think calculation is not logical and it is time consuming.

If you want to know if two attributes are corelated, calculating the correlation or covariance are very good ways to get an answer.

You can write a short program that make a loop over all your attributes and calculate the correlation of each of them to the target attribute. Then you just run it and you will see which attributes are more correlated to the target attribute.

This is not time consuming. And why it would not be “logical”?

OK, thanks. I should write a program to calculate it.

Sir, I am doing M.tech( CSE).. I want to do my research on Proactive risk management on banking delivery channels (i.e ATM , net-banking , mobile- banking etc) can i use Data Mining for this?? or plz suggest me sm data mining technique or tool, which i can use to analyze banking data

I’m not much familiar with banking. But data mining can be used on any kind of data. So there should not be any problem.

Maybe classifications algorithms like neural networks, SVM etc. could be a good technique if you want for example to classify the customers as at risk or low risk.

Hello prof Philippe,

I’ll be starting my PhD. Topic which i have selected is “Improving existing data mining algo for big data on mapreduce ” what do you think about this topic? Can you give me any suggestions?

It looks like a good general topic since big data is popular and map reduce is a popular technology. But it is still a little bit too general maybe. What kind of algorithms? What do you want to improve? Maybe you could be more specific. If you want to improve clustering algorihtms, then you could say that in your topic. Or if you want to improve something else, then you could say it.

Hi, I came across your blog while I was searching for articles to refine my area of research which is data mining. The post is very helpful. Since data mining is sort of a new area for me and I am reading on data mining in Manufacturing and Operations Management. My aim is to optimize the production process to keep one of the the raw material (fuel) cost at minimum. I have collected 10 years of daily records and I would be pleased if you could suggest on how I can approach this from your expertise.

I am not familiar with this kind of problem. It seems like an optimization problem. In artificial intelligence, genetic algorithm, ant colony algorithm etc, may be used for optimizations problems, but i’m not sure that it would fit your problem.

Thanks Philippe your blog is very helpful. I did Msc computer sc and I want my PHD Topic to focus on improving data mining techniques for Big data. I am apply for PHD and I dont have a supervisor yet. So far I have been familiarizing myself with several data mining algorithms but because of time can you guide me somehow on data mining techniques whose improvement can be good for big data analysis.

Thanks for your comment on the blog. I cannot recommend much about that because data mining is very broad. With respect to big data, you could improve any kind of algorithms: clustering, pattern mining, classification, stream mining, outlier detection, etc. etc. In other words, most topics in data mining can be combined with big data. So you should choose something that you like. I would just recommend to continue reading and try to focus on a topic that you like.

ok. Thanks.

Hi sir, I came across your blog while I was searching for articles on data mining. This is really a kind contribution.. I have planned to do work on Educational data mining, i will get students data including social and economic attributes and then find their effects on grades, finally suggest some ways of improving grades. I want to know is this enough for an MS thesis or some thing more should be done???? I have also seen some other researchers done same work but mine data set and attributes will be different. Please give me some kind suggestion..

Hi,I am searching about topic for thesis of phd on the other hand, I love datamining with .may you help me how can these fields combine together , many professors said to me that not can define the topic in level of phd about datamining please guide me

It takes time to find a topic. I cannot do it for you. You can read the blog post on top of this page that explain how to do it.

hello sir..really a nice article.. i am doing my masters in computer science and for my thesis i am interested in data mining and specially in web mining….. as i was starting from a scratch this helped me a lot… thank you…..

truly helpful article for beginners …starting from the scratch

I’m an undergraduate.I hope to do my final year research in data mining.I think to do identify hidden purchase pattern of hotels customers through the data mining.sir,can you give some advices to successful my research?

Hi Sir please tell me what is the missing on text mining topics related to Arabic language. I’m really confused about selecting my master topic. I have read about Ontology and summarization topics but until now the idea or the topic title is now clear for me. So, kindly please guide me on my research topic with respect of Arabic language text mining and NLP techniques. Thanks,,,

Hi. Sorry, I cannot find a topic for you.

Your Blog is very good for young researcher. I am working on frequent pattern mining. Can you suggest from where I got quality work related to this topic. Thanking you

Hi, thanks. I would recommend to look at the papers published in top conferences: KDD, PKDD, PAKDD, ICDM, CIKM, ADMA, etc. and also published in top journals: TKDE, TKDD, DMKDD, etc. You may find many papers on Google Scholar. I would say try to look at what the people have been doing recently to find something that you can do.

Sir i want prepare one journal on data mining..its a part of our syllabus..so could u please suggest one topic on data mining..and also the procedure for preparing a journal. Thank you

I don’t know what you want. You can take any topic if you don’t care…. classification, clustering, pattern mining. Whatever you like.

I have to prepare one research paper.I had gone through some research papers related to data mining in health domain. Sir I was wondering if you could possibly suggest one topic on data mining in health domain.

Sorry but I don’t work in the health domain. Moreover, as said in the blog post, finding a good topic takes time.

Thank you sir for your replies.Please continue the same in future also. I am doing ph.d and want to do research in clustering. Kindly guide me.I want to do on medical data.

My area has ben changed now as my Guide is changed. he told me to look for “Big data and web mining”. I have only one month to prepare the synopsis… Should I go in Content web mining or usage web mining. Which one is easier to work with? I want to take social networking sites for this. Will it be fine?

Kindly Guide me.. Regards

Don’t know which one is easier. Web mining is a popular topic, so it should be fine. But to do data mining you need to get some data. For social networks, there a website call SNAP that gives some datasets. https://snap.stanford.edu/data/

I m Farhad and working as a DWH Engg and my skill set is ETL, Oracle SQL,PL/SQL, Teradata and Unix.

so can you please suggest the topic in DWH, I know ETL very well.

current working in eBay for e-commerce project.

No, I cannot suggest any topic. It takes time to find a good research topic. You may follow the step in the blog post.

Hi,I am searching about topic for thesis of phd on the other hand, I love datamining with .may you help me how can these fields combine together ,memory efficiency of frequent subgraph mining algorithms for the case of uncertain data with applications in social network is it correct or not.if it is corrctet how to get the information about the problem tell me suggestion .thanq.

You need to read the papers on topic to know if someone has already done that or not and to know if you like the topic or not. I did not read much on this topic so I cannot tell you. But if you like subgraph mining, then, you can find some problems in this area. To find papers, just search on Google or Google Scholar and read it.

For a MS thesis, the requirements depends on the university. Some university may have lower or higher requirements. The topic looks interesting but you may discuss with your supervisor to know if it is enough. If someone has done it before it is not so original but if you can show something new in it, it would be better.

Thanks sir..

Greetings of the Day!

Was searching for topic for my thesis on Data Mining and came across this site. Very useful indeed. 🙂

I have been able to get transnational data of ATM of a bank which I got with recommendation from my college for the sole thesis purpose only.

Basically, I want my Thesis report could suggest bank’s manager to predict how much Money is necessary to be kept in ATM Vault for a particular day as more money kept is no good.

Now I want your suggestion whether I should stick to only One Algorithm and find the solution Or Should I analyze various Prediction Algorithms and find the best one among them?

As being an Average Joe, I have to research and get into with numerous algorithms and techniques for comparative analysis of algorithms and afraid i miss my thesis deadline as well.

Lastly, if anything else can be done with this transaction data apart from vault cash prediction, please suggest me so that I can think other aspects as well for my thesis.

Looking forward for positive response.

I would suggest to first start with an algorithm to get a first solution. Then, after that you can see if you can improve the work by using different algorithms. But before starting with a first algorithm you still need to read a little bit to choose that algorithm well.

Other topic? Yes certainly. You could for example, try to find the optimal moment for refilling the ATM with more cash. Should we fill it everyday in the morning ? or … ? I think that you may find other variations of this problem.

Thank you for your valuable suggestion.

Hello sir…Thank you for this blog post. I am new in data mining and and i find this field very interesting and want to do my thesis in this but unfortunately i am not getting any support from anywhere.I have learnt about all algorithms in theory and now i am planning to work on missing value imputation but i am not sure about is it a good field to proceed. I am not getting help from anywhere. Any of your suggestion would be of great help to me. Thank you.

My advice: try to look at recent research on this topic. If there are recent research on this topic in good conferences/journals, then it is a good topic. Otherwise, it may be an older topic. But there are certainly some challenges to solve. My final advice: work hard. The topic is important but how hard you work is also important to do good research.

Thank you sir.

i am kasthuri. How will i select a research topic in data mining?

You can read the blog post on top of this page.

i am kasthuri. How will i select a good research topic in data mining?

What about reading the blog post on top of this page?

I wish to my part time time ph.d in data mining. give some ideas about how to choose research topics

Did you read the blog post on top of this page?

Hello Sir , i need your help I am doing master of computer Engineering i want list of topic in data mining for dissertation.Please help me as soon as possible . Thank You Vikas

No. I won’t do that. Searching for a topics takes a time. You need to do it by yourself.

hello sir.. good afternoon… i have some confusion about selecting area of data mining. i like data mining but some people told me that data mining is very common to do research ..so give me some positive response.

Yes, data mining is popular and it is good to work in a popular field. It means that you may eventually find a job in this field and that more people may read your research. It is much better to work on something popular than on something that is not.

thank you sir.. sir give me some current research topic in data mining.. i mean i read many paper but i am very confused to select some topic. give me some proper topic from current research.

It’s better that you choose a topic that you like. But if you really want some topics, I can give you some random topics: fuzzy sequential rules mining, incremental sequential rule mining etc.

Sir,,, i m in last year so i want to project on data mining so plz suggesst me sutaible topic in this area.

I want to do Phd. I cleared written exam for Phd and my interview schedule on 14th of July. In Interview,I have to represent my broad area of research interest.

After go through lots of Research Papers, I decide my broad area of research interest is Mining Web Log Files to improve the performance of website. Sir I want to use existing technique and make some modification in it and obtain better results.

But Sir, I am really confuse in how to justify my research methodology in interview. What should i say when they ask question like “what kind of techniques you want to use?” Because I have no Idea. Please Sir Help. Reply ASAP.

Congratulations for your interview. I think you need to read a few paper on weblog mining to find what are the main techniques. It is better if you can talk about a few techniques even if you just know the basic idea. For example, one technique that may be used is sequential pattern mining (discovering sequences frequently appearing in a set of sequences). It could be used to find what occurs often in the weblog to understand the behavior. Another technique is clustering if you want to find similar web users. Or if you want to predict what will be clicked next, you may use something like our CPT+ model: http://www.philippe-fournier-viger.com/PAKDD2015_sequence_prediction.pdf Those are just some example. There are many techniques that could be applied on weblog data. Actually choosing a technique depends on WHAT you want to do (what is your goal). Saying that your interest is “mining log files” does not mean much. What is your goal? Do you want to find patterns? Do you want to cluster the users? Do you want to predict what people will click on next? … etc. etc. I think that you need to define more clearly what is your goal. It may be more important than talking about the techniques.

Hello to everyone , sir I want to do mtech thesis in data mining . sir can u suggest some topics for thesis / project

Superb work sir really appreciating while reading .

Sir, now i am final year MCA..i can choose my project in Research area..But i cant no idea about that..But,still i have learned data mining i like to doing in my project also data mining technique in Clustering also..So,If i want to one Topic for my project..Pls,Say sir….

You can choose any recent paper published about clustering in a good conference and journal and improve their work.

Hello sir , I read all your suggestions about research topics i am an m.tech student i have studies grid clustering in minor thesis now i have to work on major thesis can u kindly suggest related topic plz plz plz i want to discuss it with you how can i discuss. i will be very thankfull.

Hi, I don’t work on grid clustering so I don’t know much about which topics are good related to grid clustering. Moreover, it takes times to find a topic. I cannot suggest topics.

Respected Prof.Phillippe,

I really appreciated your suggestion, you are guiding all of the needy people you did a great work sir. I also need your help in my research i will be very thankful. I have some topic in my mind but want to discuss for proceeding further.

Thank you so much for replying sir i just want suggestion about any open source tool of clustering which can perform grid clustering also, if you can help only any tool name i will work on it myself. i will be very thankful.

I don’t know about open-source tool for grid clustering.

But in my SPMF library, there are a few clustering algorithms : K-Means, DBScan, … Perhaps that some may be useful

thank you sir i have studied comparison of some algorithms ,what can i further do in clustering or few of its algorithms .Is there any other option in clustering for my research.

There is a lot of thing that one can do in clustering: make faster algorithm, make better clustering algorithm, apply clustering algorithm in new ways, combine clustering with some other techniques, etc. As I said in the blog post, to find a good topic, one need to read several papers, and then you may get some ideas about what other people have been doing recently.

Thank u for your tips to start our Phd. Sir can suggest some topics related to data mining in biological data .

In the blog post on top of this page, I explain how to find a topic. I cannot do it for you.

Ok sir thank you

Hello sir, I am studying M.sc 2yr,i have one research paper(project) for this sem sir…i don’t know that which area i select but i like to do in data mining..i know only little bit of knowledge in data mining sir…could u help me sir for which topic that iwill select in data mining..

The blog post on top of this page explains how to choose a topic.

Sir I want to start my thesis.. i want to work on data Mining specificly on clustering can u suggest me where to start or reach to find best problem?

Look at recent papers published in top data mining conferences and papers published in top data mining journals.

Sir, I am working in a telecom company for the last 10+ years and mainly in the IT wing. Now I wish to do a Phd in large data analysis for which I am having the required data. I am not having a correct idea regarding the way I have to proceed. Can you guide me in this regard. Thanking you

Your research supervisor should be able to help you.

HI. I want to do a M.Sc. in data mining security . But it is difficult to choose a topic. There are too many data mining topics. Is it a good idea?

Yes, it is a good area. Too many topics is good, it means that you can choose. You just need one topic.

hey Philippe,

i wanted to know whether it is necessary to have a good knowledge in statistics and maths to pursue research work in data mining? am interested in data mining but i don’t have good hold on stats and maths.. could you please suggest me?

Hi, It certainly helps. But it is not a requirement. You can choose a topic that is more algorithmic oriented for example and where there is less math and statistics. For example, if you work on designing algorithms for “clustering” or “pattern mining”, you can do something with not much math.

Thank you…that helped a lot

Hey Philippe, i wanted to know whether it is possible to combine classification and regression as hybrid model if i want to have to enhance classification prediction or regression models ,could you please suggest me some knowledge needed and tools .

Hey Philippe, i wanted to know whether it is possible to combine classification and regression as hybrid model if i want to enhance classification or regression prediction models ,could you please suggest me some knowledge needed and tools .

Hi, It is certainly possible. Actually, regression can be used to performed classification. For example, you may want to guess what is the salary of some employee and you have the salary of other employees from the same area. You may do some regression to get that information, and it could be seen as classification. This is just a simple example, but there are certainly more complex ways. Since I don’t do research on that topic, I cannot say too much about that. You would need to search a little bit and read recent papers to know more about what has been done recently.

I’m doing phd in big data mining. I wan to work on any real time scenario for research. Can you suggest me specific dataset/problem for it?

This is very broad. You could do for example stream mining to detect security attacks by analyzing network traffic. I think that there are such datasets available about network trafic. Besides, I think that whatever the topic that you choose, something important is to find some datasets whether from some companies, make your own datasets or download some public datasets.

hello sir, can u suggest any topic for research even other then data mining i needed it for my final year thesis…im really confused since last few days unable to find any topic..

If you don’t care about the topic, you can choose a data mining topic such as fuzzy sequential rule mining. This topic has probably not been done.

thnx a lot sir….. 🙂

I’m Selva from India,I’m interested in doing my Ph.D work in Natural Disaster Management using spatial data mining.Since the natural disaster management is a vast area I’m struggling to drill down into the exact area and also I’m worried about getting the proper data-sets on this area. Could you please suggest your ideas on this.

Thanks, Selva

Hi, I’m not familiar with this area. Maybe you should try to talk with some special about natural disaster. If there is some at your university, you may try to contact them and perhaps that they can provide some data. Also, maybe look at some government websites. Some places like Japan or Taiwan have many earquakes. I think you should be able to get data from these governement websites about the location and time of each earthquakes if you search carefully for example.

Thanks for the suggestion.

Regards, Selva

Dear Sir Thanks a lot for your such helps. A lot of students are facing these types of problems and you are helping in a very effective way. We are grateful to you. Could you tell me which are the latest research areas of data mining? In which conferences or journals I can find what people have been doing recently on data mining so that I can choose an area or topic on data mining for my PhD program?

I will be grateful if you help me in this regard.

Thanks………

Hi You are welcome. You may look at the data mining conferences : KDD, PAKDD, ICDM, PKDD, ICDE, CIKM, DAWAK, DASFAA, DEXA, ADMA, etc. also the data mining journals: TKDE, DMKD, TKDD, KAIS, etc.

There is a lot of possible topics. Some current trends are big data, social networks, etc. But you can also choose something else.

Thank you again sir…………

Sir..i want to do some work in data mining for my phd…sir my programming skill is weak .sir plz suggest me topic to research purpose which has less programming

If you are doing a PhD in computer science, you will certainly need to do some programming and need to improve your programming skills if they are not so good. Data mining in general requires to be good at programming especially if you want to design some algorithms, because you will want to make algorithms that are fast, since data mining algorithms are generally applied on large amount of data. If you are not good at programming, perhaps that you should choose a topic that invovle less programming such as research on e-learning, or you could still do data mining but more toward the applications of data mining rather than working on algorithms. For example, you could work on applying data mining to biomedical data, biology, e-learning etc. If you are good at math, another option is to take more of a math approach and use software such as Mathlab, etc.

Thanks a lot sir

Sir which language is used in data mining algorithm…can i learn this language at home?if i want to learn language for data mining ..which language i should learn..which book prefer to leraning that language…

Dear Sir I studied different papers from difference conferences and journals given by you. Thanks a lot for your suggestions. Actually I have very limited knowledge on data mining but I am interested too much to do my research in this area. I am ready to study a lot. But I have to prepare a PhD proposal within very short time. Now I guess I can work on “Spammer Detection Methods/mechanisms/techniques in social media systems or something related this”. But I can not be enough confident what should/might be the proper topic. Could you please help me in proper direction for choosing a topic in this regard.

I need PhD spicific topic in healthcare data mining, please

Hello sir, I am studying M.s.c and decision to do my research in data mining direction particular in revenue accounting fraud statement detection please sir i want to determine the algorithm that achieve my word correctly and display acceptance result thanks

Dear sir, I want to do some work for my phd in data mining especially sequential pattern mining on Big Data but i could not find any new specific problem in this area. would you please help me and suggest your idea to find a specific problem on this ?

Thanks you.

Hello, there are a lot of possibilities. You can combine any two topics to obtain a new topic. For example, I did not see any algorithms for sequential rule mining in big data. Designing a sequential rule mining algorithm running on big data framework such as Hadoop or Spark is a good topic. Besides, that there are many other possibilities. For example, you could combine : incremental mining + sequential pattern mining + big data, or any other combinations of topics.

Thanks a lot Sir.

It helps me a lot.

i interested in work on data mining . Also i have many years experience in fields of education. please introduce some topics about data mining in education. thank you.

You may read papers from the Educational Data Mining conference (EDM), or related conferences such as Artificial Intelligence in Education (AIED), Intelligent Tutoring Systems (ITS), and others to see what are the current problem related to data mining and education.

Sir, I want to do research in Parallelisation of Data Mining Algorithms. I am also good in Optimization techniques like Genetic algorithms. Please suggest me topic related to this. Thanks in advance…

I suggest to read recent papers in data mining conferences/journals to find a good topic. It takes time to find a good topic.

Actually I want to work on Distributed framework or shared frame work of Data mining algorithms like clustering and classification algorithms either on Hadoop Mapreduce or using OpenMP/MPI, but which application I choose to apply data mining clustering or classification algorithm, I am little bit confused. I do not want to go towards Semantic web mining … Please suggest…..

Data mining can be applied in almost any domains: bioinformatics, medecine, psychology, education, etc. In my opinion, you should choose a domain (1) that you like, and (2) where you can easily collect data to be used by your data mining algorithms.

Sir , I want to work on agricultural data.please suggest current topic. am are work “study of agricultural land soil using classification techniques” in M.Phil. program. I am look a P.Hd Topic in data mining .

You need to find a topic by yourself. I cannot help you.

Sir, I want to do P.hd in data mining.Please suggest me title for my work

Algorithms for sequential rule mining in data streams

Hi Sir , would you please suggest me a problem in high utility sequential pattern mining ?

Thanks alot

You can combine high utility seq. pattern mining with any other topic such as fuzzy pattern mining, incremental pattern mining, stream mining, etc. You may read other papers and try to combine two ideas to get a problem.

would you please suggest me a topics for thesis in information technology major in business analytics.

thanks a lot.

I don’t really have time to find topics for other people than my own students. Sorry. You should read papers in recent conferences and journals and choose a topic that you like and try do to better or do something that other people did not do.

Hello sir, I have bit knowledge in Data Mining techniques. If i apply DM techniqes and do some analysis on any real time problem, does it consider for PhD. because i am not doing anything new,just applying DM techniques and getting some reslut. Please help me in this regard

It can be. The goal of a Ph.D. is to contribute to the advancement of knowledge in some field. You may either work on more fundamental problems or working on some more applied problems. For example, one may design a new data mining algorithm (a more fundamental problem), but one may also use existing data mining techniques do something new or better in some fields (for example, use existing algorithms in a new way to identify the authors of texts, or to discover communities in social networks). What is important is that a PhD need bring something novel. So if you work on an applied problem, you still need to bring some novelty but at the application level. Personally, I prefer topics at a fundamental level (design of algorithms) but there many people who work on a more applied level (for example, you may check any conference on e-learning, there is a lot of applied research).

Hi how are you?i hope u are doing well actually im really interested in data mining topics such as clustring ,data mining in big data ive read many articles but the problem is i could’t find something interesting what sub-topics in “data mining in big dat”a that can i work on ?

could you help me with

I cannot find a topic for you. You may ask help from your supervisor. He should be able to help you. Otherwise, you may look at recen data mining conferences to look for some topics. “data mining” and “big data” is very broad. It can mean basically anything in data mining.

im afraid that i would spend more time looking for topic,and after that ill find that the topic is already solved.

i wan work on something likes this in general :processing big data and finding the relations and interesting pattern is difficult ,So ,what kind of challenges theta we maybe face when processing big date and how can we provide efficient data mining techniques to overcomes the issues? would be a good PhD topic? or i have to look to something different? thank in advance.

Before doing any research project, one should read enough papers about the topic to know what already exist, and make sure that the problem has not been solved. If you want to become a researcher, this is something that you need to learn to do. Even if it takes a lot of time, you still need to do it, and it is important that you do it.

Ok, so you want to find patterns or relationships in big data… But that is still not a topic because it is too general. Finding patterns in big data could mean almost any data mining algorithms applied to big data. So you still did not define a topic. To define a topic, you would need to 1) define what kind of patterns you want to find: Clusters, outliers, frequent patterns, subgraphs, communities, etc., 2) and define in what kind of data: graphs, transaction databases, multimedia data, spatial data, data streams, etc. and 3) perhaps also what kind of approach you want to use to find these patterns.

So I cannot say whether it is a good topic because your topic is still too general. Finding a good topic takes time. There are an infinite amount of “good topics” that one could find. But as I said before, I cannot do the litterature review for you. You need to do it by yourself and evaluate by yourself if it is a good topic. Even in data mining, I’m not familiar with all the topics, and I will not read the papers for you to tell you if it is a good topic or not, unless your ask me about something that is directly related to what i’m doing.

that’s really helpful .thank you very much.

Please suggest me application of confabulation association rule mining for multidimensional association rule generation.

I did not read about “confabulation association rules” so I cannot say much about it. You may ask the authors of that paper.

hi Philippe when i am reading your answer to the comments , now i am sure about the topic must be specific and not general .

what i want is i need to do classification on cloud data can you help in this way

preparing a paper of applying classification techniques on cloud data

thanks a lot

Good that you know more about how to look for a topic. But I cannot help further than that. I am very busy and I need to take care of my own research.

i am very much interest to purse my PhD degree in Data Mining field. i need your guild lines to select my PhD topic in opinion mining and sentiment analysis. please share some recent research areas in this field.

I am very interested to pursue research in Data Mining in Healthcare Science Applications, Please suggest me some recent popular topics where I can explore to start my research.

i dont work on this topic. You may look at recent research papers.

Hello Sir, I am a computer science student, entering into research for the first time. I have been searching for topics in data mining. I wanted to ask what are the topics related to stocks that I can research on or write a thesis on. There is a lot of research going in this field. I m really confused on which direction to go- something less explored and with a lot of scope of research. It will be great if you could suggest an approach on this topic.

P.S. If you have any suggestion on a ‘popular topic’ -as you suggested, that would make my way easier to some conferences, would be of great help.

It would be hard to suggest topics about stocks since I don’t work on that. But some typical problems that have previously seen are to predict the stock market, detect fraud on the stock market, find correlations or patterns indicating that some stocks are related and behave the same way on the stock market…. I think you could find more ideas by searching a little bit on Google Scholar

Best regards

Thanks for your helpful suggestion.

Do you know anybody who is currently working on this topic who i can ask questions from? I wish to get more insight on this.

No. I don’t personally know anybody working on that. But if you find some papers about stock markets on Google Scholar, you can always send e-mail to the authors to ask them questions or ask for their dataset. Sometimes they will give you some advice or maybe even their datasets.

Thank You once again 🙂

Hi Philippe, Iiam looking forward my PhD in DataMining. Iam interested in Social Newtwork Mining. How and where can we apply datamining algorithms ( clustering, classification algorithms, etc.) on Social Network Analysis. And also how can we apply Neural Networks on datamining applications.

Best regards.

Hi, Wish you good luck for you Ph.D. This is some very broad questions. For applications of data mining on social networks, you could have a look at the papers published at the ASONAM conference ( international conference on Advances in Social Network Analysis and Mining ) which is about data mining and social networks. You will see that there are many topics. It would be too long to list all of them here. You could also check journals related to that topic and papers on Google Scholar.

Neural networks also have hundreds of applications. Basically, you could see neural networks as a data mining technique. So the applications of neural networks in data mining, would be all the applications of neural networks, and there are a lot.

Thanks alot Philippe for your valuable suggestion.

i want good thesis topic in data mining for ph.d

then you may read the blog post on that page which explain how to find a topic

sir am doing m.phil, my guide suggest to learn scikit website. pls suggest some topic regarding this.

Scikit is just a tool that you may use in your research. It can be helpful. But you should perhaps find a topic first instead of looking at scikit to find a topic. That is my opinion

Thank you sir.I decided to do in medical field. Is it correct decision ? and now is it current trend? I am just confused sir. Please suggest me some of the field which are in current trend.

I don’t work in the medical field. I work in the field of data mining. The medical field is a possible application of data mining. If you can get some real medical data and know some people in the medical field that can guide you in your project, then why not choosing that field. I guess it could be ok. But even if you choose a field, you still need to find a specific topic. The “medical field” is still very broad and could lead to an infinite amount of topics. I suggest to read papers in recent conferences and journals related to your interest to see what people are working on.

Ok sir. Data mining is my domain sir, in that i decided to do healthcare or big data application. Please tell me which is sufficient to collect data. Also please suggest me some of the other application which are more effective.

“big data application” or “healthcare” is still very broad. You need to read some recent papers and see what other researchers are doing to find a good topic. I cannot do that for you. It can take actually quite a lot of time to find a good topic. But you can always ask your supervisor to help you about that.

sir , this blog is really useful. I am planning to do datamining in the feild of nano technology . any ideas or suggestions of topics related to this .plz suggest.

Glad you like the blog.

I don’t work on nanotechnology.

Hi. Right now I’m planning on continue my research study on malware analysis. I’m thinking of using data mining approach in my research. However, since i’m doing in PhD level, my supervisor do not want me just applying the data mining technique. He always asked me about the weakness/limitations I’ve found in data mining. However, as far as i’m concern, there is none limitations on data mining. So, may I asked if you or any visitors in this blog have any idea about the data mining weaknesses/limitations in malware analysis that I have overlooked?

Thanks again.

I understand what you mean. At first, it may seems that a technique has no limitations. But there is always some. What you could do is to choose a technique from the literature, try it, and after you tried it, you will certainly think about some way to improve it. For example, if you try some classification technique to classify malwares, then what about considering also the time dimension? what about doing real-time classification? what about developing an approach where your algorithm can say how many percent certain he is that a program is a malware? etc. There is generally always some way to extend a technique for example by considering more information such as time etc. Besides, another possibility is to combine characteristics from different approaches that you like. By reading a few papers, you may try to see the different approach for classifying malwares and then try to combine the best characteristics of all these approaches. This is just some ideas.

Thanks for your quick reply. But I have questions about combining a characteristic from different approaches like you said. I had confused with the meaning of characteristic? is it characteristic of the malware or the technique in classification it use (eg c4.5, dt etc) ? and by mention different approaches, did you mean by combining statistic and machine learning? I’m so sorry for asking but I’m really stress right now. I’m glad you’re replying me.

I mean, it could be different characteristics of the data mining approach. For example, maybe there is an algorithm A for classification, and another algorithm B for classification that consider time, and another algorithm C for classification that let you add constraints when doing the classification. If you think that your approach need classification + time + constraint, you could try to combine the algorithms A + B + C in a single new algorithm. This is just some general ideas. In general, the more information you consider such as time, the more complicate the problem become, and then you need to extend the original algorithms to consider this additional information. I just tell you this as some general idea. You could see how that could apply to your problem by reading some papers and comparing the different data mining approaches to then try to take the best of each approach if possible. Or try to add something new that other people did not do.

Thank you so much for replying. I’m feeling great I have stumble upon your website today. Thanks again. Your website is awesome. May God bless you & your family.

You are welcome. Good luck. By the way, you may also check my data mining forum:

http://forum.ai-directory.com/list.php?5

It is a place for discussing data mining.

Dear sir, presently i am teaching the pg and ug students for the c omputer science , and i had choosen to work for my PH.D. from different university privately. This is an request kindly suggest the topics which is convenient in teaching and working both related to my ph.d. work.

Finding a topic takes time, so I will not do that for you. You can read the blog post at the top of this page which explains how to search for a topic.

I like your collection, thanks for sharing this wonderful collection of themes with us.i am working in Cloud Erp Software Companies In Chennai

Hi Sir, I Rasheeduddin registered Ph.D In Data Mining Clustering My Title is

“Improving level of efficiency through K Means Algorithm in Social Networking Data Base”.

Actually i want to implement the K Means Algorithm in Social Networking Data Base.

Please Suggest me any changes required to this Title and what type of Back Ground work is required to start. please guide me/ suggest me

Sir, I have Qualified in OU Ph.D Entrance test. I want to do Ph.D on data mining. so please tell me the topic of research on data mining or any other area.

Thank you sir

Could you please suggest me where I can get more information and where I can get the recent works on opinion mining?

Hi, I would recommend to use Google Scholar to search for papers. There is a features that let you search by year. You can search for papers from 2012- 2016 for example using Google Scholar. Otherwise, you may also check the journals and conference on data mining or similar research areas. But, Google Scholar is a very good place for searching for articles.

Thanks……..Thanks a lot you again.

Can you please tell me also some journals and conferences on pattern recognition???

I don’t work on pattern recognition so I cannot say too much about that. But you may check some pages like this one which provides some kind of rankings of conferences/journals:

http://www.scimagojr.com/journalrank.php?category=1707

Thanks again. Really I am grateful to you…………

my area of interest is bank data mining can you please suggest some topic on it ?

I don’t work on bank data, so it is hard for me to suggest something. But a typical topic for bank data is to evaluate the credit record of customer to detect whether they will pay back their debt or not for example. There exists some papers on that already. You may find using Google Scholar.

hi could you suggest some topic for ” Data mining for customer relationship management” please

Sir, I have Qualified in Ph.D Entrance test. I want to do Ph.D on data mining. I am interested to do phd in revenue management/system by using data mining technique . can u guide me this interest is good or bad for the project work

The blog post above gives you some guidance. Have you read it?

I want to do Mtech Research in data mining. please suggest me a topic in data mining which is somehow related to networking.

I suggest to read the blog post on top of this page that explains how to find a topic. 🙂

need opinion, target of my topic is “user identification over social network”. i just want to apply changes through these techniques “clustering and outliers”. sir kindly tell what is the scope of topic? if its fine then please generate impressive topic name for me.

I don’t know what you want me to tell you about the “scope of this topic”.

But if you choose this topic, the topic name could be “Novel clustering and outlier detection approaches to user identification in social networks”.

Thanx , Philippe Founier-Viger

SIR what are the current problems in identification of user in social networks, which problems should i target?

I want to get example title for my thesis in computer science.I am interested in data mining.Please sir.

If you want some example of titles, you may look at Google Scholar. There are a lot of examples. Or you may look at data mining journals and conferences. There are a lot of examples.

Which method I can used for passport data analysis? Please guided me the latest or pooular method for that.

I never worked on passport analysis. It depends what you want to do. Analyzing a passport to do what? If you want to analyze the picture, then you could use some image processing techniques. But it really depends what you want todo. The best would be that you search what other people have been doing on this topic recently in Google Scholar for example. As I said, I don’t work on this topic, so i cannot tell you the method that people have been using.

Hello sir I want to discover the travel pattern from passport data analysis.

I want to discover the travel pattern from passport data analysis.So which method I can used sir?

Many methods could be applied: – Find people with similar travel patterns –> some clustering – Find people with abnormal travel patterns –> outlier detection – Find some frequent travel patterns –> pattern mining – Classify the travellers based on their travel patterns –> classification

This is just some basic idea. Those area some of the most popular areas in data mining. Depending on how your data looks like etc, you may choose different techniques.

Yes thank you sir.

Please, guidence for the new method(update method) for this.My reference paper used like k-means, apriori algorithm but this methods are olds.So my supervisor told me to used new method.I want to apply the update method for this topics.

To apply some new method, you first need to know what the researchers have already done before you. So as I said previously, what you need to do is to read some research papers about discovering travel patterns. Then, you can understand what other people have done and do something new or something different. There is no way to avoid that step. If you ask me to recommend you something new, I cannot really tell you because I did not read the papers on discovering travel patterns, and I don’t know what people have done already. This, you need to do it by yourself.

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Dear professor, i have interest association rule mining (frequent, periodic, utility, and closed patterns mining). could you suggest me the mathematical and statistical portion for study to design new algorithm on the base of exiting algorithm like (Apriori, Eclat, and fp-Growth).

i will be thankful for this help,

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Sunday 9 November 2014

Thesis topics on data mining with abstract, 2. effort estimation for object-oriented system using stochastic gradient boosting technique, the success of software development depends on the proper prediction of the effort required to develop the software. project managers oblige a solid methodology for software effort prediction. it is particularly paramount throughout the early stages of the software development life cycle. faultless software effort estimation is a major concern in software commercial enterprises. stochastic gradient boosting (sgb) is a machine learning techniques that helps in getting improved estimated values. sgb is used for improving the accuracy of estimation models using decision trees. in this paper, the basic aim is the effort prediction required to develop various software projects using both the class point and the use case point approach. then, optimization of the effort parameters is achieved using the sgb technique to obtain better prediction accuracy. furthermore, performance comparisons of the models obtained using the sgb technique with the other machine learning techniques are presented in order to highlight the performance achieved by each method., 3. classification and clustering using intelligent techniques: application to microarray cancer data, analysis and interpretation of dna microarray data is a fundamental task in bioinformatics. feature extraction plays a critical role in better performance of the classifier. we address the dimension reduction of dna features in which relevant features are extracted among thousands of irrelevant ones through dimensionality reduction. this enhances the speed and accuracy of the classifiers. principal component analysis is a technique used for feature extraction which helps to retrieve intrinsic information from high dimensional data in engine spaces to solve the curse of dimensionality problem. neural networks and support vector machine are implemented on reduced data set and their performances are measured in terms of predictive accuracy, specificity, and sensitivity. next, we propose a multi objective genetic algorithm-based fuzzy clustering technique using real coded encoding of cluster centers for clustering and classification. this technique is implemented on microarray cancer data to select training data using multi objective genetic algorithm with non-dominated sorting. the two objective functions for these multi objective techniques are optimization of cluster compactness as well as separation simultaneously. this approach identifies the solution. support vector machine classifier is further trained by the selected training points which have high confidence value. then remaining points are classified by trained svm classifier. finally, the four clustering label vectors through majority voting ensemble are combined. the performance of the proposed moga-svm, classification and clustering method has been compared to moga-bp, svm, bp. the performance is measured in terms of silhoutte index, ari index respectively. the experiment was carried on three public domain cancer data sets, viz., ovarian, colon and leukemia cancer., 4. evaluation of software understandability using software metrics, understandability is one of the important characteristics of software quality, because it may influence the maintainability of the software. cost and reuse of the software is also affected by understandability. in order to maintain the software, the programmers need to understand the source code. the understandability of the source code depends upon the psychological complexity of the software, and it requires cognitive abilities to understand the source code. the understandability of source code is get effected by so many factors, here we have taken different factors in an integrated view. in this we have chosen rough set approach to calculate the understandability based on outlier detection. generally the outlier is having an abnormal behavior, here we have taken that project has may be easily understandable or difficult to understand. here we have taken few factors, which affect understandability, an brings forward an integrated view to determine understandability., 5. adequate test data generation using evolutionary algorithms, software testing is a approach where different errors and bugs in the software are identified. to test a software we need the test data. in this thesis, we have developed the approach to generate test data automatically from some initial random test data using evolutionary algorithms (ea) and test the software to detect the presence of errors, if any. we have taken two measures; they are path coverage and adequacy criterion to test the validation of our approach. in our first approach, we have used simple genetic algorithm (ga) to find the test data. we then used an mimetic algorithm to curb the difficulties faced by using ga. we are using the instrumented program to find the paths. we then represent the program into a control flow graph (cfg). we have used genetic algorithm to find the more optimal test data that covers all the feasible test paths from some initial random test data automatically. path coverage based testing approach generates reliable test cases. a test case set is reliable if it's execution ensures that the program is correct on all its inputs. but, adequacy requires that the test case set detect faults rather than show correctness. hence, for adequacy based testing we uses the concept of mutation analysis. here, we have taken the mutation score as our fitness function in the approach. we find out the mutation score from using mutation testing based tool called "mujava". and then generate test data accordingly. we applied a more complex hybrid approach to generate test data. this algorithm is a hybrid version of genetic algorithm. it produces better results than the results generated by using ga. also it curbs various problems faced by ga, no comments:, post a comment.

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MTech Thesis In Data Mining

MTech Thesis In Data Mining can do by all post graduate final year students. We offer M.Tech thesis with efficient solving problem approach than under graduate projects. We support M.Tech students to analyze various issues in computing environment, network, security and mining applications. M.Tech students submit their thesis based on issues previous algorithm and ensure better solution to overcome this problem. We provide M.Tech thesis which is used for future reference for next year students. Our main aid of M.Tech thesis is resource sharing strengthen telecommunication process signals, energy conservation and enhance overall system performance.

M.TECH THESIS IN DATA MINING

M.Tech related thesis in computing environment:

We support M.Tech students are studying various type of computing environment such as mobile computing, grid computing, peer to peer computing and cloud computing from Elsevier papers. These applications are not provide accurate result due to lack of security, high volume of power consumption and resource scarcity.

The problems are described as below:

We provide M.Tech thesis with symmetric & asymmetric algorithm for high level secure data storage & transmission. We implement RSA, DES, and AES and triple DES, Homomorphic encryption and diffie Hellman key exchange algorithm.

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We handle major problem in computing environment is resource scarcity. We refer resource as memory, CPU, and network bandwidth which equally distributes resources to all computing user is very teddies process. We overcome resource scarcity by introducing efficient virtual machine migration technique.MTech Thesis In Data Mining

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We implement computing environment composed of multiple computer to process user request which consume high level power reduce power utility of computing environment by giving energy efficient and power aware mechanism. In our thesis we identify idle power consumption system & cut down power to save energy level.

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We propose data, text and image mining application which is an prominent area in mining application. We ensure fast, accurate information retrieval in big data environment which is an challenging task of data mining researchers. We introduce map reduce framework in data mining to reduce server node workload & increase retrieval process speed. We implemented efficient scheduling algorithm and developed more than 70+ thesis in mining to enhance system performance.

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Techsparks – Phd & M.Tech Thesis Help online

At techsparks you can take the plunge and get the dissertation guidance you need from a professional dissertation guide., exploring thesis topics in data mining: unveiling patterns, insights, and applications.

Choosing the appropriate topic is crucial if you want to explore the world of data mining for your thesis. It is the process of discovering patterns, correlations, and insights from large sets of data by using statistical, machine learning, and database systems. It helps in making informed decisions by transforming raw data into meaningful information. In this blog, we will explore the thesis topics in data mining including spatial data mining, pattern mining, distributed data mining, and text mining. These topics encompass a wide range of applications for data mining, highlighting its versatility and impact across various fields. Each topic offers the potential for significant contributions to both theoretical and practical aspects of data mining.

Innovative and Impactful Thesis Topics in Data Mining:

Data mining:

Data mining deals with sorting, filtering, and classifying data from huge datasets to reveal subtle relationships and patterns, which enables identifying businesses and solving complicated business challenges via data analysis. Data mining software techniques and tools enable organizations to foreknow future market trends and create business-critical decisions at crucial times.

Spatial data mining:

Spatial data mining deals with the process of discovering concealed patterns and relationships in geospatial data. Geospatial data contain information that is related to particular locations or geographic coordinates. This can involve data from numerous sources like GPS data, census data, satellite imagery, and social media check-ins.

Pattern mining:

Pattern mining is a key area in data mining that focuses on discovering patterns and regularities in large datasets. These patterns can be associations, sequences, trends, or structures that reveal important insights about the data. Pattern mining has various applications in fields such as market basket analysis, bioinformatics, web mining, and network security.

Distributed data mining:

The distributed data mining process requires the mining of distributed datasets stored in multiple local databases. Frequently the data is distributed between various databases, which makes it more vulnerable to security threats. By using distributed data mining, the executive can perform data analysis and mining operations in a distributed manner to find knowledge and utilize it more effectively for business operations.

Text mining:

Text mining is also called text analysis. It converts unstructured text into structured data for easy analysis. Text mining uses (NLP) i.e., natural language processing that enables machines to process human language automatically.

The exploration of thesis topics in data mining underscores the transformative potential of this field, aligning with the guidance provided by TechSparks . Our team of experts is on standby, ready to support you in completing your project within the designated time frame, ensuring your research endeavors are met with timely and expert guidance.

DATA MINING

DATA SCIENCE
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CONTROL SYSTEMS
DC – AC CONVERTERS
DC – DC converters
DISTRIBUTION SYSTEMS
HYBRID SYSTEMS
MULTILEVEL CONVERTERS
POWER QUALITY
SOLAR POWER GENERATION
WIND POWER GENERATION
Bio Medical
Electrical Embedded
EMBEDDED APPLICATIONS
INDUSTRIAL AUTOMATION
Telecommunication
Wireless Communication
5G NETWORKS
ARTIFICIAL NEURAL NETWORK
DEEP LEARNING
DEVICE-TO-DEVICE COMMUNICATIONS
IMAGE ENHANCEMENT
IMAGE SEGMENTATION
MIMO TRANSMISSIONS
ARITHMETIC CORE
CADENCE ORIENTED
COMMUNICATIONS AND CRYPTO CORE
COMMUNICATIONS
CORE MEMORIES
DESIGN FOR TESTABILITY
FPGA APPLICATIONS
LOW POWER VLSI
MATLAB APPLICATIONS
QCA NANO TECHNOLOGY
TRANSISTOR LOGIC
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Mobile Computing
information security
BIG DATA & HADOOP
NETWORKING ON NS2
NETWORKING & SECURITY
VEHICULAR TECHNOLOGY
CIVIL M.TECH PROJECT
Image processing
Signal Processing
Digital Image Processing
Power Electronics
VLSI Back end
VLSI low power
Control System
Power Electronics 2018-2019
Power Generation 2018-2019
Power System 2019
Digital System
Power Converter
Power System
Wind Power Generation
EMBEDDED PROJECT
MECHANICAL M.TECH PROJECT
Synopsis List 2019-20
Synopsis List 2018-19
Android(BE)
Cloud computing
Dot Net project
Core vlsi BE
Vlsi backend Be
Vlsi low power BE
Communication BE
Wireless communication BE
Digital image processing BE
Image processing BE
Power Electronics BE
Signal Processing BE
Robotics BE
Embedded BE
Mechanical 2018-19
Civil engineering
Electrical engineering
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Branch: Computer Science

Topic: M.Tech DATA MINING Projects(2018-19)


DST KT C 01	Characterizing and Countering Communal Micro-blogs During Disaster Events
DST KT C 02	Correlated Matrix Factorization for Recommendation with Implicit Feedback
DST KT C 03	Hashtagger+: Efficient High-Coverage Social Tagging of Streaming News
DST KT C 04	A New Query Recommendation Method Supporting Exploratory Search Based on Search Goal Shift Graphs
DST KT C 05	Optimizing Quality for Probabilistic Skyline Computation and Probabilistic Similarity Search
DST KT C 06	Serendipitous Recommendation in E-Commerce Using Innovator-Based Collaborative Filtering
DST KT C 07	SIMkNN: A Scalable Method for In-Memory kNN Search over Moving Objects in Road Networks
DST KT C 08	Top-k Durable Graph Pattern Queries on Temporal Graphs
DST KT C 09	Topology-driven Diversity for Targeted Influence Maximization with Application to User Engagement in Social Networks
DST KT C 010	Webpage Depth Viewability Prediction using Deep Sequential Neural Networks
DST KT C 011	DeepClue: Visual Interpretation of Text-based Deep Stock Prediction
DST KT C 012	A Survey of Location Prediction on Twitter
DST KT C 013	An Iterative Classification Scheme for Sanitizing Large-Scale Datasets
DST KT C 014	Collaborative Filtering-Based Recommendation of Online Social Voting
DST KT C 015	Computing Semantic Similarity of Concepts in Knowledge Graphs
DST KT C 016	Detecting Stress Based on Social Interactions in Social Networks
DST KT C 017	Dynamic Facet Ordering for Faceted Product Search Engines
DST KT C 018	Mining Competitors from Large Unstructured Datasets
DST KT C 019	Continuous Top-k Monitoring on Document Streams
DST KT C 020	Personal Web Re-visitation by Context and Content Keywords with Relevance Feedback
DST KT C 021	Energy-efficient Query Processing in Web Search Engines
DST KT C 022	Clustering Data Streams Based on Shared Density between Micro-Clusters
DST KT C 023	Booster in High Dimensional Data Classification
DST KT C 015	Efficient Algorithms for Mining Top-K High Utility Itemsets
DST KT C 025	Domain-Sensitive Recommendation with User-Item Subgroup Analysis
DST KT C 026	DARE: A De-duplication-Aware Resemblance Detection and Elimination Scheme for Data Reduction with Low Overheads
DST KT C 027	A Mixed Generative-Discriminative Based Hashing Method
DST KT C 028	DiploCloud: Efficient and Scalable Management of RDF Data in the Cloud
DST KT C 029	Location Aware Keyword Query Suggestion Based on Document Proximity
DST KT C 030	Nearest Keyword Set Search in Multi-Dimensional Datasets
DST KT C 031	Top-Down XML Keyword Query Processing
DST KT C 032	Top-k Spatio-Textual Similarity Join

Topic: DATA MINING (2017-18)

S.No.	Titles	Download
	DATA MINING
DST 1CP DM 1	Keyword Search on Temporal Graphs
DST 1CP NS2 2	Efficient Keyword-Aware Representative Travel Route Recommendation
DST 1CP NS2 3	An Approach for Building Efficient and Accurate Social Recommender Systems using Individual Relationship Networks
DST 1CP NS2 4	DRIMUX: Dynamic Rumor Influence Minimization with User Experience in Social Networks
DST 1CP NS2 5	User-Centric Similarity Search
DST 1CP NS2 6	Probabilistic Models For Ad Viewability Prediction On The Web
DST 1CP NS2 7	Understand Short Texts by Harvesting and Analyzing Semantic Knowledge
DST 1CP NS2 8	RAPARE: A Generic Strategy for Cold-Start Rating Prediction Problem
DST 1CP NS2 9	l-Injection: Toward Effective Collaborative Filtering Using Uninteresting Items
DST 1CP NS2 10	Towards Real-Time, Country-Level Location Classification of Worldwide Tweets
DST 1CP NS2 11	Temporal Conformance Analysis and Explanation of Clinical Guidelines Execution: an Answer Set Programming approach
DST 1CP NS2 12	Dynamic Facet Ordering for Faceted Product Search Engines
DST 1CP NS2 13	SociRank: Identifying and Ranking Prevalent News Topics Using Social Media Factors
DST 1CP NS214	Improving Customer Relationship Management Using Data Mining

DST TO DM 15	Mining the Most Influential k-Location Set From Massive Trajectories
DST TO DM 16	Energy-efficient Query Processing in Web Search Engines
DST TO DM 17	Advanced Block Nested Loop Join for Extending SSD Lifetime
DST TO DM 18	Feature Selection by Maximizing Independent Classification Information
DST TO DM 19	A Systematic Approach to Clustering Whole Trajectories of Mobile Objects in Road Networks
DST TO DM 20	An Efficient Indexing Method for Skyline Computations with Partially Ordered Domains
DST TO DM 21	Continuous Top-k Monitoring on Document Streams
DST TO DM 22	Dynamic Facet Ordering for Faceted Product Search Engines
DST TO DM 23	Efficient Algorithms for the Identification of Top-k Structural Hole Spanners in Large Social Networks
DST TO DM 15	Feature Constrained Multi-Task Learning Models for Spatiotemporal Event Forecasting
DST TO DM 25	Spectral Ensemble Clustering via Weighted K-means: Theoretical and Practical Evidence
DST TO DM 26	Analyzing Sentiments in One Go: A Supervised Joint Topic Modeling Approach
DST TO DM 27	Data-driven Answer Selection in Community QA Systems
DST TO DM 28	Efficient Top-k Dominating Computation on Massive Data
DST TO DM 29	Online Multi-Task Learning Framework for Ensemble Forecasting
DST TO DM 30	Search Rank Fraud and Malware Detection in Google Play
DST TO DM 31	User Vitality Ranking and Prediction in Social Networking Services: a Dynamic Network Perspective
DST TO DM 32	Collaboratively Training Sentiment Classifiers for Multiple Domains
DST TO DM 33	Semiring Rank Matrix Factorisation
DST TO DM 34	Scalable Algorithms for CQA Post Voting Prediction
DST TO DM 35	On Fault Tolerance for Distributed Iterative Dataflow Processing
DST TO DM 36	Geo-social Influence Spanning Maximization
DST TO DM 37	Efficient Keyword-aware Representative Travel Route Recommendation
DST TO DM 38	Differentially Private Data Publishing and Analysis: a Survey
DST TO DM 39	App Miscategorization Detection: A Case Study on Google Play
DST TO DM 40	Adaptive ensembling of semi-supervised clustering solutions
DST TO DM 41	Searching Trajectories by Regions of Interest
DST TO DM 42	Personal Web Revisitation by Context and Content Keywords with Relevance Feedback
DST TO DM 43	Enhancing Binary Classification by Modeling Uncertain Boundary in Three-Way Decisions
DST TO DM 44	Engagement dynamics and sensitivity analysis of YouTube videos
DST TO DM 45	Discovering Newsworthy Themes From Sequenced Data: A Step Towards Computational Journalism
DST TO DM 46	On Spectral Analysis of Signed and Dispute Graphs: Application to Community Structure
DST TO DM 47	Human-Powered Data Cleaning for Probabilistic ReachabilityQueries on Uncertain Graphs
DST TO DM 48	Keyword Search on Temporal Graphs
DST TO DM 49	Profiling Entities over Time in the Presence of Unreliable Sources
DST TO DM 50	Influence Maximization in Trajectory Databases
DST TO DM 51	User Vitality Ranking and Prediction in Social Networking Services: a Dynamic Network Perspective
DST TO DM 52	Predicting Persuasive Message For Changing Students Attitude Using Data Mining
DST TO DM 53	Experimental Analysis Of Data Mining Application For Intrusion Detection With Features Reduction
DST TO DM 54	Applying Data Mining Techniques In Cyber Crimes
DST TO DM 55	Probabilistic Models For Ad View ability Prediction On The Web
DST TO DM 56	Mining Competitors from Large Unstructured Datasets
DST TO DM 57	GALLOP: Global feature fused Location Prediction for Different Check-in Scenarios
DST TO DM 58	Efficient Clue-based Route Search on Road Networks
DST TO DM 59	Microscopic and Macroscopic spatio-temporal Topic Models for Check-in Data
DST TO DM 60	Large-scale Location Prediction for Web Pages
DST TO DM 61	Modeling Information Diffusion over Social Networks for Temporal Dynamic Prediction
DST TO DM 62	Discrete Nonnegative Spectral Clustering
DST TO DM 63	Managing Temporal Constraints with Preferences: Representation, Reasoning, and Querying
DST TO DM 64	Facilitating Time Critical Information Seeking in Social Media
DST TO DM 65	Stochastic Block modeling and Variational Bayes Learning for Signed Network Analysis
DST TO DM 66	Finding Related Forum Posts through Content Similarity over Intention-based Segmentation
DST TO DM 67	Big Search in Cyberspace
DST TO DM 68	l-Injection: Toward Effective Collaborative Filtering UsingUninteresting Items
DST TO DM 69	Towards Real-Time, Country-Level Location Classification of Worldwide Tweets
DST TO DM 70	Differentially Private Data Publishing and Analysis: a Survey
DST TO DM 71	Scalable Algorithms for CQA Post Voting Prediction
DST TO DM 72	Adaptive ensembling of semi-supervised clustering solutions
DST TO DM 73	A Multi-objective Optimization Approach for Question Routing in Community Question Answering Sevices
DST TO DM 74	Search Rank Fraud and Malware Detection in Google Play
DST TO DM 75	Data-Driven Answer Selection in Community QA Systems
DST TO DM 76	Analyzing Sentiments in One Go: A Supervised Joint Topic Modeling Approach
DST TO DM 77	Energy-efficient Query Processing in Web Search Engines
DST TO DM 78	Online Multi-task Learning Framework for Ensemble Forecasting
DST TO DM 79	Query Expansion with Enriched User Profiles for Personalized Search Utilizing Folksonomy Data
DST TO DM 80	Reducing Uncertainty of Probabilistic Top-k Ranking via Pairwise Crowd sourcing
DST TO DM 81	An Approach for Building Efficient and Accurate Social Recommender Systems using Individual Relationship Networks
DST TO DM 82	A Probabilistic Model of Social Working Memory for Information Retrieval in Social Interactions
DST TO DM 83	Gracker: A Graph-based Planar Object Tracker
DST TO DM 84	Efficient High Utility Pattern Mining for Establishing Manufacturing Plans with Sliding Window Control
DST TO DM 85	Information Seeking in Online Healthcare Communities: The Dual Influence From Social Self and Personal Self
DST TO DM 86	Joint Alignment of Multiple Point Sets with Batch and Incremental Expectation- Maximization
DST TO DM 87	Predicting Social Emotions from Readers’ Perspective
DST TO DM 88	Public Interest Analysis Based on Implicit Feedback of IPTV Users
DST TO DM 89	Uncertain Data Clustering in Distributed Peer-to-Peer Networks
DST TO DM 90	Mining Fashion Outfit Composition Using an End-to-End Deep Learning Approach on Set Data
DST TO DM 91	Supervised and Unsupervised Aspect Category Detection for Sentiment Analysis with Co-Occurrence Data
DST TO DM 92	Design and Implementation of an RFID-Based Customer Shopping Behavior Mining System
DST TO DM 93	A Natural Language Processing Framework for Assessing Hospital Readmissions for Patients with COPD
DST TO DM 94	Event Detection and User Interest Discovering in Social Media Data Streams
DST TO DM 95	Approaches to Cross-Domain Sentiment Analysis: A Systematic Literature Review
DST TO DM 96	Building and Querying an Enterprise Knowledge Graph
DST TO DM 97	A Novel Variable Precision Reduction Approach to Comprehensive Knowledge Systems
DST TO DM 98	Multi-Target Regression via Robust Low-Rank Learning
DST TO DM 99	New Splitting Criteria for Decision Trees in Stationary Data Streams
DST TO DM 100	A Systematic Review on Educational Data Mining
DST TO DM 101	Mining Human Activity Patterns from Smart Home Big Data for Healthcare Applications
DST TO DM 102	Random Forest Classifier for Zero-Shot Learning Based on Relative Attribute
DST TO DM 103	Scientific Workflow Mining in Clouds
DST TO DM 104	Mining Online Discussion Data for Understanding Teachers’ Reflective Thinking
DST TO DM 105	Majority Voting and Pairing with Multiple Noisy Labeling
DST TO DM 106	Detecting Stress Based on Social Interactions in Social Networks
DST TO DM 107	Probabilistic Models For Ad View ability Prediction On The Web
DST TO DM 108	Modeling and Learning Distributed Word Representation with Metadata for Question Retrieval
DST TO DM 109	Collaborative Filtering-Based Recommendation of Online Social Voting

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Latest Data Mining Research and Thesis Topic Guidance For M.Tech and
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Latest Research and Thesis topics in Data Mining
Topics to study in data mining. Data mining is a relatively new thing and many are not aware of this technology. This can also be a good topic for M.Tech thesis and for presentations. Following are the topics under data mining to study: Fraud Detection. Crime Rate Prediction.
82 Data Mining Essay Topic Ideas & Examples
Commercial Uses of Data Mining. Data mining process entails the use of large relational database to identify the correlation that exists in a given data. The principal role of the applications is to sift the data to identify correlations. A Discussion on the Acceptability of Data Mining.
Data Mining
An exploration and evaluation of concept based interpretability methods as a measure of representation quality in neural networks Author: Remmits, Y. L. J. A., 30 Sept 2019 Supervisor: Menkovski, V. (Supervisor 1) & Stolikj, M. (External coach) Student thesis: Master
Data Mining
Data Mining also finds its application in Machine Learning, pattern recognition, database management and artificial intelligence. Thesis, Project and Research Ideas/Topics in Data Mining. Following is the list of data mining thesis ideas and research topics: Data Leakage Detection; Database Text Mining; Web Content Analysis; Social Media Mining
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Integration of MapReduce, Amazon EC2, S3, Apache Spark, and Hadoop into data mining. These are the recent trends in data mining. We insist that you choose one of the topics that interest you the most. Having an appropriate content structure or template is essential while writing a thesis.
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information regarding degree topics stays up to date. 2.2 Data Mining Overview Data mining is the process of discovering patterns and relationships in large volumes of data by using methods from the areas of computer science, statistics and artificial intelligence [12]. Data mining is a general term and it can often be confusing. Moreover,
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If you are from the computer science field, "Data Mining" name surely known for you. Surely you have some basic knowledge of data mining and database. Data m...
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MTech Thesis In Data Mining MTech Thesis In Data Mining can do by all post graduate final year students. We offer M.Tech thesis with efficient solving problem approach than under graduate projects..
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Latest Thesis Topics in Data Mining - Free download as PDF File (.pdf), Text File (.txt) or read online for free. The document discusses some of the key challenges students face when writing a thesis in data mining. It notes that selecting a relevant and up-to-date topic is difficult given the evolving nature of data mining. Extensive research is also demanding and requires a strong ...
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MTech Thesis In Data Mining
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Choosing the appropriate topic is crucial if you want to explore the world of data mining for your thesis. It is the process of discovering patterns, correlations, and insights from large sets of data by using statistical, machine learning, and database systems. It helps in making informed decisions by transforming raw data into meaningful information.…
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Data Mining
These project topics are very helpful in deciding your M.TECH THESIS Topic in the field of DATA MINING Projects.DST Arena is having innovative ideas to shape your career with our projects. We provide CSE PROJECTS support at an affordable cost for the students. ... Topic: DATA MINING (2017-18) S.No. Titles: Download: DATA MINING: DST 1CP DM 1 ...
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