Data Warehousing & Data Mining (Elective-II)

Unit I

Data Warehousing: Overview, Definition, Data Warehousing Components, Building a Data Warehouse, Warehouse Database, Mapping the Data Warehouse to a Multiprocessor Architecture, Difference between Database System and Data Warehouse, Multi-Dimensional Data Model, Data Cubes, Stars, Snow Flakes, Fact Constellations.

Unit II

Data Warehouse Process and Technology: Warehousing Strategy, Warehouse Management and Support Processes, Warehouse Planning and Implementation, Hardware and Operating Systems for Data Warehousing, Client/Server Computing Model, Parallel Processors & Cluster Systems, Distributed DBMS implementations, Warehousing Software, Warehouse Schema Design.

Unit III

Data Mining: Overview, Motivation, Definition & Functionalities, Data Processing, Forms of Data Pre-processing, Data Cleaning (Missing Values, Noisy Data — Binning, Clustering, Regression), Inconsistent Data, Data Integration and Transformation, Data Reduction — Data Cube Aggregation, Dimensionality Reduction, Data Compression, Numerosity Reduction, Discretization and Concept Hierarchy Generation, Decision Tree.

Unit IV

Classification & Clustering: Data Generalization, Analytical Characterization, Analysis of Attribute Relevance, Mining Class Comparisons, Statistical Measures in Large Databases, Statistical-Based / Distance-Based / Decision Tree-Based Algorithms. Clustering: Similarity and Distance Measures, Hierarchical and Partitional Algorithms, CURE, Chameleon, DBSCAN, OPTICS, STING, CLIQUE, Model-Based Methods. Association Rules: Large Item Sets, Basic / Parallel / Distributed Algorithms, Neural Network Approach.

Unit V

Data Visualization and Trends: Aggregation, Historical Information, Query Facility, OLAP Functions and Tools, OLAP Servers — ROLAP, MOLAP, HOLAP, Data Mining Interface, Security, Backup and Recovery, Tuning and Testing Data Warehouse. Warehousing Applications and Recent Trends: Web Mining, Spatial Mining, Temporal Mining.