KMBNIT05 Business Data Warehousing & Data Mining

14 Sep 202124 Feb 2022

Unit 1 Data Warehousing [Book]
Data Warehousing: Overview, Definition	VIEW
Data Warehousing Components	VIEW
Difference between Database System and Data Warehouse	VIEW
Data Warehousing Characteristics, Functionality	VIEW
Data Warehousing Advantages	VIEW
Metadata: Concepts and Classifications	VIEW
Multi-Dimensional Data Model, Data Cubes, Stars, Snow Flakes, Fact Constellations	VIEW
Concept hierarchy, 3 Tier Architecture, ETL, Data Marting	VIEW
Use of Data warehousing in Current Industry Scenario, Case Study	VIEW

Unit 2 [Book]
Data Visualization and Overall Perspective	VIEW
Aggregation, Query Facility	VIEW
OLAP function and Tools: OLAP Servers, ROLAP, MOLAP, HOLAP	VIEW
Data Mining interface, Security, Backup and Recovery	VIEW
Tuning Data Warehouse, Testing Data Warehouse	VIEW
Warehousing applications and Recent Trends	VIEW
Types of Warehousing Applications, Web Mining	VIEW
Spatial Mining and Temporal Mining	VIEW

Unit 3 [Book]
Data Mining: Overview, Motivation, Definition	VIEW
Data Mining Functionalities	VIEW
Difference between Data mining and Data Processing	VIEW
KDD process	VIEW
Form of Data Preprocessing	VIEW
Data Cleaning: Missing Values, Noisy Data, Binning, Clustering, Regression, Computer and Human inspection, Inconsistent Data, Data Integration and Transformation	VIEW
Data Reduction: Data Cube Aggregation, Dimensionality reduction, Data Compression	VIEW
Application of Data Mining	VIEW

Unit 4 [Book]
Data Mining Techniques: Data Generalization, Analytical Characterization	VIEW
Analysis of attribute relevance, Mining Class comparisons	VIEW
Statistical measures in large Databases, Statistical-Based Algorithms, Distance-Based Algorithms	VIEW
Association rules: Introduction, Large Item sets, Basic Algorithms, Apriori Analysis	VIEW
Generating Filtering Rules, Target Marketing, Risk Management, Customer profiling	VIEW

Unit 5 [Book]
Decision Tree-Based Algorithms Classification	VIEW
Clustering: Introduction, Hierarchical Algorithms	VIEW
Similarity and Distance Measures	VIEW
Partitioned Algorithms	VIEW
Hierarchical Clustering: CURE and Chameleon	VIEW
Parallel and Distributed Algorithms	VIEW
Neural Network approach	VIEW
Data mining Case study	VIEW
Applications of Data Mining	VIEW
Introduction of data mining tools like WEKA, ORANGE, SAS, KNIME etc.	VIEW

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