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CSS3206: DATA MINING

Core/Elective: Elective Semester: 2 Credits: 3

Course Description

Data mining is the science of extracting hidden information from large datasets. This course offers clear and comprehensive introduction to both data mining theory and Practice. All major data mining techniques will be dealt with and how to apply these techniques in real problems are explained through case studies.

Course Objectives

Introduce the fundamental concepts of data and data analysis.
Learn data mining components like model representation, score functions for fitting models to data, optimization and search techniques.
Case based study of specific data mining tasks like Clustering, Classification, Regression, Pattern Discovery and Retrieval by Content.

Course Content

1. Fundamentals of data mining – components of data mining algorithms – Data measurement Strategies – Data quality – Tools for displaying data – Principle Component Analysis – Dealing with uncertaininty – Automation – hypothesis testing.

2. Overview of Data mining algorithms – Tree classifies – Artificial neural Networks – Support vector machines – Association rule mining – Case study.

3. Models and patterns – fundamentals of modeling – Model Structures for production models for probability Distribution and Density functions – Models for Structures – scoring functions – Seeking models with different complexities – Evaluation of models and pattern.

4. Searching for models and patterns – State-space search – Greedy search – parameter optimization methods – EM algorithm – Descriptive modeling probability Distribution- pattern based cluster analysis – Hierarchical clustering – classification modeling – Tree models – Predictive modeling for regression – linear models .

5. Web Data Mining – web content mining – web usage mining – Web Structure mining – Search Engines – Search engine Architecture – Ranking of Web pages – Text retrieval – Image retrieval – time series and sequential retrieval – Case study,

REFERNCES

1. Principles of Data mining: David Hand. Heikki Mannila, Padhraic Smyth Prentice Hall India (2007)
2. Data mining methods and Techniques: A B M Showkat Ali, Saleh A Wasimi, Cengage Learning (2009)
3. Introduction to Data mining with case studies: G.K Gupta PHI (2008)


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