UNIT I Data Warehousing, Business Analysis and On - Line Analytical Processing (OLAP) Basic Concepts - Data Warehousing Components - Building a Data Warehouse - Database Architectures for Parallel Processing - Parallel DBMS Vendors - Multidimensional Data Model - Data Warehouse Schemas for Decision Support, Concept Hierarchies - Characteristics of OLAP Systems - Typical OLAP Operations, OLAP and OLTP. (Chapter - 1) UNIT II Data Mining - Introduction 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. (Chapter - 2) UNIT III Data Mining - Frequent Pattern Analysis Mining Frequent Patterns, Associations and Correlations – Mining Methods - Pattern Evaluation Method – Pattern Mining in Multilevel, Multi Dimensional Space – Constraint Based Frequent Pattern Mining, Classification using Frequent Patterns. (Chapter - 3) UNIT IV Classification and Clustering Decision Tree Induction, Bayesian Classification - Rule Based Classification - Classification by Back Propagation - Support Vector Machines - Lazy Learners - Model Evaluation and Selection - Techniques to improve Classification Accuracy. Clustering Techniques - Cluster analysis - Partitioning Methods - Hierarchical Methods - Density Based Methods - Grid Based Methods - Evaluation of clustering - Clustering high dimensional data - Clustering with constraints, Outlier analysis - Outlier detection methods. (Chapter - 4) UNIT V WEKA Tool Datasets - Introduction, Iris plants database, Breast cancer database, Auto imports database - Introduction to WEKA, The Explorer - Getting started, Exploring the explorer, Learning algorithms, Clustering algorithms, Association - rule learners. (Chapter - 5)
