Introduction to Data Mining Systems – Knowledge Discovery Process – Data Mining Techniques – Issues – applications- Data Objects and attribute types, Statistical description of data, Data Preprocessing – Cleaning, Integration, Reduction, Transformation and discretization, Data Visualization, Data similarity and dissimilarity measures.

Basic Concepts: Data Warehousing: A multitier Architecture, Data warehouse models: Enterprise warehouse, Data mart and virtual warehouse, Extraction, Transformation and loading, Data Cube: A multidimensional data model, Stars, Snowflakes and Fact constellations: Schemas for multidimensional Data models, Dimensions: The role of concept Hierarchies, Measures: Their Categorization and computation, Typical OLAP Operations.

Market Basket Analysis – Frequent Item Set Mining methods – Apriori algorithm – Generating Association Rules – A Pattern Growth Approach – Association Analysis to Correlation 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 propagation- Support Vector Machines – Lazy Learners- Genetic Algorithm – Experiments with Weka (Iris plants dataset)

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