7 CS 2-DATA MININIG AND WAREHOUSING |
Units: I |
Overview, Motivation(for Data Mining),Data Mining-Definition & Functionalities, Data Processing, 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, Numerosity Reduction, Clustering, Discretization and Concept hierarchy
generation.
|
Units: II |
Concept Description:- Definition, Data Generalization, Analytical Characterization, Analysis of attribute relevance, Mining
Class comparisons, Statistical measures in large Databases. Measuring Central Tendency, Measuring Dispersion of Data,
Graph Displays of Basic Statistical class Description, Mining Association Rules in Large Databases, Association rule mining,
mining Single-Dimensional Boolean Association rules from Transactional Databases– Apriori Algorithm, Mining Multilevel
Association rules from Transaction Databases and Mining Multi- Dimensional Association rules from Relational Databases. |
Units: III |
What is Classification & Prediction, Issues regarding Classification and prediction, Decision tree, Bayesian
Classification, Classification by Back propagation, Multilayer feed-forward Neural Network, Back propagation Algorithm,
Classification methods K-nearest neighbor classifiers, Genetic Algorithm. Cluster Analysis: Data types in cluster analysis,
Categories of clustering methods, Partitioning methods. Hierarchical Clustering- CURE and Chameleon. Density Based
Methods-DBSCAN, OPTICS. Grid Based Methods- STING, CLIQUE. Model Based Method –Statistical Approach, Neural
Network approach, Outlier Analysis
|
Units: IV |
Data Warehousing: Overview, Definition, Delivery Process, Difference between Database System and Data Warehouse,
Multi Dimensional Data Model, Data Cubes, Stars, Snow Flakes, Fact Constellations, Concept hierarchy, Process
Architecture, 3 Tier Architecture, Data Marting.
|
Units: V |
Aggregation, Historical information, Query Facility, OLAP function and Tools. OLAP Servers, ROLAP, MOLAP, HOLAP,
Data Mining interface, Security, Backup and Recovery, Tuning Data Warehouse, Testing Data Warehouse.
|
|