Introduction to data mining system-Knowledge Discovery Process-Data Mining Technologies-Issues-Application-Cleaning Integration Reduction Transformation Discretization-Data Visualization -Data similiarity dissimiliarity measures

Basic Concepts-Data Warehousing-Multitier Architecture-Data warehouse models-Enterprise Warehouse-Datamart and Virtual Warehouse,Extraction, Transformatiion and loading,Data cube : A multi-dimensional data model,*,Snowflakes and fact constellations:Schemas for multi-dimensional data models,dimension: the role of concept hierarchies,measures: Their categorization and computation, Typical OLAP operations

Market Basket Analysis- Frequent item set mining method - APRIORi algorithm - Generating association rules-Pattern growth approach-Association analysis to Co-relation analysis-Explore Weka and run APRIORI algorithm with different support and confidence values(Supermarket dataset)

Basic concepts- Decision Tree Induction - Bayes Classification Methods- Rule based Classification - Model Evaluation and Selection - Techniques to improve Classification Accuracy - Classification by back propogation - Support Vector Machines Lazy Learners- Genetic Algorithm - Experiments with Weka ( Iris plants dataset)

Basic issues in clustering - Partitioning methods : K- means, K-Medoids - Agglomerative Hierarchial Clustering - DBSCAN - Cluster Evaluation - Density based clustering - Grid Based Methods - Evaluation of clustering - Explore clustering techniques available in Weka(Breast cancer dataset)