The sourse is designed for the students whi are facing learning issues in understanding the DM concepts. The course starts with the basics and gradulally moves to intermidiate level

Following topics will be covered as a part of this course

DATA MINING AND DATA WAREHOUSING
1. Introduction and Data Preprocessing : Why data mining, What is data mining, What kinds of data can be mined,
What kinds of patterns can be mined, Which Technologies Are used, Which kinds of Applications are targeted,
Major issues in data mining .Data Preprocessing: An overview, Data cleaning, Data integration, Data reduction, Data transformation and data discretization.

2. Data warehousing and online analytical processing: Data warehousing: Basic concepts, Data warehouse modeling: Data cube and OLAP, Data warehouse design and usage, Data warehouse implementation, Data generalization by attribute-oriented induction,

3. Classification: Basic Concepts: Basic Concepts, Decision tree induction, Bays Classification Methods, Rule-Based classification, Model evaluation and selection, Techniques to improve classification accuracy

4. Cluster Analysis: Basic concepts and methods: Cluster Analysis, Partitioning methods, Hierarchical Methods, Density-based methods, Grid-Based Methods, Evaluation of clustering