KMBNIT05 BUSINESS DATA WAREHOUSING AND DATAÂ MINING UNIT 1 Data Warehousing Data Warehousing : Overview and definition, Data warehousing Components Difference between Database System and Data Warehouse Data Warehouse Characteristics , Functionally , Data Warehouse Advantage Metadata: Concepts and Classifications, Multi-Dimensional data model, Data Cubes, Stars, Snow flakes, Fact Constellations Concepts Hierarchy, 3 Tier Architecture , ETL, Data Marketing, Use of Data Warehousing in current Industry Senario UNIT 2Â Data Visualization and Overall Perspective Aggregation Query Facility, OLAP function and tools: OLAP servers, ROLAP, MOLAP,HOLAP. Data Mining interface , Security, Backup and Recovery Tuning Data Warehouse, Testing Data Warehouse Warehousing Application and recent trends Recent trends in data warehousing include: Types of Warehousing Application, Web Mining UNIT 3Â Data Mining: Overview, Motivation, definitions, Data Mining Functionalities Differences between Data Mining and Data Processing, KDD Process Data Cleaning: Missing values, Noisy Data, Binning, Clustering, Regression Computer and Human Inspection, Inconsistent Data, Data Integration and Transformation Application of Data Mining UNIT 4Â Data mining Techniques : Data Generalization, Analytical Characterization Analysis of attribute relevance , Mining Class Comparison Statistical measures in large database , Statistical - based algorithms , Distance based algorithms Association rules: Introduction, Large item sets, Basic algorithms, Apriori Analysis Generating Filtering Rules, Target Marketing, Risk Management, Customer profiling UNIT 5Â Decision Tree based Algorithms Classification Clustering introduction, Hierarchical Algorithms Similarity and Distances Measured, Partitioned Algorithms Hierarchical Clustering : CURE and Chameleon, Parallel and Distributed Algorithms, Neural Network Approach Data Mining Case Study, Application of Data Mining, Introduction of Data Mining tools like WEKA, ORANGE, SAS, KNIME etc.
