Introduction

Kinds of data and patterns to be mined, Basic statistical description of data.

Data Preprocessing

Data objects and attributes, Data similarity and dissimilarity, Data cleaning, Data integration, Data reduction, Data transformation and discretization.

Data Warehousing

Data warehouse modeling, Design issue, Implementation and usage, Data mining, Associations, Correlations, Mining methods, Pattern evaluation.

Data Classification

Decision tree induction, Classification methods, Evaluation and selection of classification, Classification accuracy.

Cluster Analysis

Partitioning, Hierarchy, Density and grid based clustering methods, evaluation of clustering methods, Cluster quality.