Chapter 1: Introduction Motivation, major issues, major applications, characteristics
Chapter 2: Data warehouse Model, architecture, operations
Chapter 3: Data pre-processing Data cleaning, data transformation, data reduction
Chapter 4: Association Rules Apriori, single-pass frequent itemset mining, FP-Growth
Chapter 5: Classification Decision tree, Bayesian Classifier, Classification by backpropagation, KNN classifier, statistical prediction models
Chapter 6: Clustering Partitioning methods, hierarchical methods, density-based methods, grid-based methods