2K6 CS 805(F) DATA MINING AND DATA WAREHOUSING

Module I (14 hours)
Fundamentals of Data Mining-What is data mining, Data mining strategies(Mining Frequent pattern, Association, classification & prediction, cluster analysis)-classification of data mining systems-major issues in data mining-Data preprocessing-Data mining applications. Data warehouse & OLAP technology- What is data warehouse, Multi dimensional data model, star, snowflakes and fact constellations, OLAP operations in Multidimensional data model- Data warehouse architecture-A three tier data warehouse architecture-Data warehouse back-end tools and utilities-types of OLAP servers.
Module II (13 hours)
Mining Frequent patterns- Frequent item sets, closed item sets and association rules, APRIORI algorithm for finding frequent item sets, Generating association rule from frequent item. Classification and Prediction-Issues regarding classification and prediction, classification by decision tree Induction, Bayesian classification, Rule based classification, SVM, k-Nearest neighbor classifiers. Prediction-Linear regression, Nonlinear regression.
Module III (13 hours)
Cluster analysis- What is cluster analysis, Type of data in cluster analysis-Categorization of major clustering Methods-classical partitioning methods- K-means and K-Medoids, Hierarchical methods-BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies)- Introduction about Density based methods, Grid based methods model based methods and outlier analysis.
Module IV (12 hours)
Introduction about Mining data streams, mining time series data, spatial data, multimedia data, text data and web (Concepts only). Introduction about WEKA Data mining tool- introduction, installation, WEKA data file format, Data visualization, Data filtering, selecting attributes, Data mining with WEKA, APRIORI algorithm through WEKA, clustering through WEKA, regression analysis through WEKA

Text books
1. Data Mining – Concepts and Techniques – Jiawei Han & Michaline Kamber Elsevier, second edition .
2. Data Mining: Methods and Techniques, ABM Shawkath Ali, Saleh A Wasimi, Cengage Learning India edn. (for WEKA data mining tool)
Reference books
1. Data Mining Introductory and advanced topics –Margaret H Dunham, Pearson Education
2. Data Mining Techniques – Arun K Pujari, University Press