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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 person) (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 person) (External coach) & Groen, J. (External person) (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 person) (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 person) (External coach)

Anomaly detection in text data using deep generative models

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

Anomaly detection on vibration data

Supervisor: Hess, S. (Supervisor 1), Pechenizkiy, M. (Supervisor 2), Yakovets, N. (Supervisor 2) & Uusitalo, J. (External person) (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 person) (External coach) & Ya-alimadad, M. (External person) (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 person) (External coach) & Veldsink, J. W. (External person) (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 person) (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 person) (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 person) (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)

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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.

For more details Contact Us.  You can call us on this number +91-9465330425 or drop an email at   [email protected]   for any type of dissertation help in India. You can also fill the query form on the website. 

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Trending Data Mining Thesis Topics

            Data mining seems to be the act of analyzing large amounts of data in order to uncover business insights that can assist firms in fixing issues, reducing risks, and embracing new possibilities . This article provides a complete picture on data mining thesis topics where you can get all information regarding data mining research

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.

Our engineers and experts can provide you with any type of research assistance on any of these data mining development tools . We satisfy the criteria of your universities by ensuring several revisions, appropriate formatting and editing of your thesis, comprehensive grammar check, and so on . As a result, you can contact us with confidence for complete assistance with your data mining thesis. What are the important data mining 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

We currently provide full support for all parts of research study, development, investigation, including project planning, technical advice, legitimate scientific data, thesis writing, paper publication, assignments and project planning, internal review, and many other services. As a result, you can contact us for any kind of help with your data mining thesis topics.

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We associated with 200+reputed SCI and SCOPUS indexed journals (SJR ranking) for getting research work to be published in standard journals (Your first-choice journal).

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Ma Dissertation Topics

Dissertation topics for mtech computer science in data mining.

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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.

Resource scarcity:

  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

Power consumption:

  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.

APPLICATIONS OF DATA MINING

M.Tech thesis based on mining application:

  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.

Network related M.Tech thesis:

    We propose network used for communication among computers and networks. We determine wireless network related thesis from research community and using efficient shortest path find algorithm as ant colony optimization, genetic algorithm and particle swarm optimization. We use this algorithm to identify & accurate packet transmission path. We compute throughput, delay, packet delivery ratio and comparison graph performance by simulation tool such as QUALNET, NS3, NS2, Opnet and OMNET++ in network related M.Tech thesis.MTech Thesis In Data Mining

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Branch: Computer Science 

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

Topic: DATA MINING (2017-18)

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