Name of Subject  : DATA MININIG AND WAREHOUSING  (7 CS 2)

Unit

Contents

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

I

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.

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

II

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.

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

III

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

Data Warehousing: Overview, Definition, Delivery Process, Difference between Database System and Data

IV

Warehouse, Multi Dimensional Data Model, Data Cubes, Stars, Snow Flakes, Fact Constellations, Concept

hierarchy, Process Architecture, 3 Tier Architecture, Data Marting.

Aggregation, Historical information, Query Facility, OLAP function and Tools. OLAP Servers, ROLAP, MOLAP,

V