M.tech thesis in data mining

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  1. A blog by Philippe Fournier-Viger about data mining, data science, big data…
  2. M.Tech (CSE) with specialization in Data Engineering
  3. Items where Subject is "Engineering and Technology > Computer and Information Science" - ethesis
Data Mining as a healthcare research tool (Analytics Techniques Listed Below)

The Euclidean distance is calculated from the centroid point to cluster similar and dissimilar points from the data set. The prediction analysis is the technique which is applied to the input dataset to predict current and future situations according to the input dataset. In the predictive analysis, the clustering is applied to cluster similar and dissimilar type of data and on the clustered data the technique of classification is applied which will classify the data for prediction analysis.

There is an array of data mining tools and techniques that keep evolving to keep pace with the modern innovations.

A blog by Philippe Fournier-Viger about data mining, data science, big data…

Problem definition — In the first phase problem definition is listed i. Data exploration — Required data is collected and explored using various statistical methods along with identification of underlying problems. Data preparation — The data is prepared for modeling by cleansing and formatting the raw data in the desired way. The meaning of data is not changed while preparing. Modeling — In this phase the data model is created by applying certain mathematical functions and modeling techniques. After the model is created it goes through validation and verification.

Evaluation — After the model is created, it is evaluated by a team of experts to check whether it satisfies business objectives or not. Deployment — After evaluation, the model is deployed and further plans are made for its maintenance. A properly organized report is prepared with the summary of the work done. Data mining is a relatively new thing and many are not aware of this technology. This can also be a good topic for M.

M.Tech (CSE) with specialization in Data Engineering

Tech thesis and for presentations. Following are the topics under data mining to study:. Data Mining is a relatively new field has a bright scope now as well as in future. The scope of this field is high due to the fact that markets and businesses are looking for valuable data by which they can grow their business. Data mining as a subject should be mandatory in computer science syllabus.


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As earlier said data mining is a good topic for an M. Tech thesis.


  • Thesis in Data Mining and preparing an Abstract for problem of M.Tech.
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  • Students can go for deep research to have a good content for their thesis report. Data Mining finds its application in Big Data Analytics. Following is the list of latest topics in data mining for final year project, thesis, and research:. Web Mining — Web mining is an application of data mining for discovering data patterns from the web. Web mining is of three categories — content mining, structure mining and usage mining.

    Content mining detects patterns from data collected by the search engine. The data collected through web mining is evaluated and analyzed using techniques like clustering, classification, and association. It is a very good topic for the thesis in data mining. Predictive Analytics — Predictive Analytics is a set of statistical techniques to analyze the current and historical data to predict the future events.

    The techniques include predictive modeling, machine learning, and data mining. In large organizations, predictive analytics help businesses to identify risks and opportunities in their business. Both structured and unstructured data is analyzed to detect patterns. Predictive Analysis is a lengthy process and consist of seven stages which are project defining, data collection, data analysis, statistics, modeling, deployment, and monitoring.

    It is an excellent choice for research and thesis. The eligibility criteria mentioned above is minimum and the disciplines may use higher cut-off levels for shortlisting. Programme Requirements. The thesis is compulsory for the award of an M. It is expected that the thesis will result in high quality scholarly publications and national and international conference presentations. The coursework, research, and thesis requirements for part-time M.

    However, part-time M. A cumulative experience of at least two years is necessary for admission to the part-time M.

    Eligible candidates admitted to full-time M. Institute encourages M. Admission is generally offered on the basis of a written test and interview. The Institute reserves the right to use its own judgment while determining the candidate's level of competency based on the available information. The final selection will be based on all of above, viz. The written test is generally based on the topics related to general aptitude logical reasoning, analytical and numerical ability, etc. The candidates who do not qualify in the written test need not appear for interview.

    The reserved category candidates will be given due relaxation in cutoff marks as per the norms. The candidates called for the Test and Interview will be reimbursed any amount exceeding Rs. For example, in case of total railway fare of Rs. It is required to produce evidence railway ticket in support of the claim. Text mining or text analytics is a process in which information is extracted from the written sources. It also transforms the unstructured text into the structured data for better analysis.

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    Items where Subject is "Engineering and Technology > Computer and Information Science" - ethesis

    The main purpose of text mining is to identify facts and relationships from the large textual data. It helps businesses and organizations to get valuable insights useful for their business. The main processes in text mining include information retrieval, lexical analysis, pattern recognition, and predictive analytics.

    The foremost step in text mining is to organize the data into a more structured form by involving the use of natural language processing technology. Text mining finds its application in sentiment analysis. Other important applications include social media monitoring, bioinformatics, scientific discovery, competitive intelligence. It is a popular area for research in data mining. Clustering is an unsupervised machine learning method to create groups of data-sets having similar patterns using statistical distribution.

    Data clustering is used in market research, pattern recognition, data analysis, and image processing. The clustering methods are classified as follows:. K-means clustering is an important type of clustering used on the undefined data. It is an unsupervised learning method. In these methods, data points are assigned to each k group.

    Social Network Analysis is also one of the popular topics in data mining for thesis and research. It is a quantitative and qualitative process that measures the flow of relationship in a social network. The relationship is represented in the form of nodes and links where nodes represent the people and links represent the relationships between the nodes. Mathematical and visual analysis of the human relationship is represented by social network analysis. Data Mining process and techniques are used in the social network analysis.

    Data mining find its application in bioinformatics. It is a field that deals in the collection, processing, and collection of the biological data.

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    There are various applications of data mining in bioinformatics such as gene finding, protein function domain detection, protein function interference. Clustering and classification methods of data mining help in microarray data and protein array data analysis. Data mining also offers a solution for analyzing large-scale biological data. It helps in the prediction of functions of anonymous genes.