Business Data Warehousing & Data Mining (IT05)

Table of Content

Unit 1: Data Warehousing: Overview, Definition, Data Warehousing Components, Difference between Database System and Data Warehouse, Characteristics, Functionality and Advantages; Metadata: Concepts and classifications; Multi-Dimensional Data Model, Data Cubes, Stars, Snow Flakes, Fact Constellations, Concept hierarchy, 3 Tier Architecture, ETL, Data Marting ,Concept Hierarchy, Use of Data warehousing in Current Industry Scenario, Case Study.
Unit 2: 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 applications and Recent Trends: Types of Warehousing Applications, Web Mining, Spatial Mining and Temporal Mining.
Unit 3: Data Mining: Overview, Motivation, Definition & Functionalities, difference between data mining and Data Processing, KDD process, Form of Data Preprocessing, Data Cleaning. : Missing Values, Noisy Data, Binning, Clustering, Regression, Computer and Human inspection, Inconsistent Data, Data Integration and Transformation. Data Reduction:-Data Cube Aggregation, Dimensionality reduction, Data Compression. Applications of Data Mining in today’s world.
Unit 4: Data Mining Techniques: Data Generalization, Analytical Characterization, Analysis of attribute relevance, Mining Class comparisons, Statistical measures in large Databases, 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 5: Classification: Definition Decision Tree-Based Algorithms, Clustering: Introduction, Similarity and Distance Measures, Hierarchical and Partitioned Algorithms. Hierarchical Clustering- CURE and Chameleon. Parallel and Distributed Algorithms, Neural Network approach, Business , Data mining Case study, Applications of Data Mining, Introduction of data mining tools like WEKA, ORANGE , SAS, KNIME etc.