Insight into data mining : Theory and practice / K.P Soman, Shyam Diwakar, V. Ajay

1. Data mining
1.1 Introduction

2. Data mining from a business perspective
2.1 Introduction
2.1 From data mining tools to solutions
2.3 Evolution of data mining systems
etc

3 : Data types, input and output of data mining algorithms
3.1 Introduction
3.2 Instances and features
3.3 Different types of features
etc

4. Decision trees- classification and regression trees
4.1 Introduction
4.2 Constructing classification trees
4.3 CGAID (Chi-square automatic interaction detection)
etc

5. Pre-processing and post processing in data mining
5.1 Introduction
5.2 Steps in pre-processing
5.3 Discretization
etc

6 : Datasets
6.1 Introduction
6.2 Contact lenses
6.3 Iris plants database
etc

7 : Association rule mining
7.1 Introduction
7.2 Automatic discovery of association rules in transaction databases
7.3 The apriori algorithms
etc

8 : Machine learning with open source and commercial
8.1 Machine learning wirh weka
8.2 XLMiner

9. :Algorithms for classification and regression
9.1 Introduction
9.2 Naïve bayes
9.3 Multiple regression analysis
etc

10: Support vector machines
10.1 Introduction
10.2 Basic ldea behind linear support vector machines
10.3 SVM with soft margin: Linear kernel
etc

11: Cluster analysis
11.1 Introduction
11.2 Partitional clustering
11.3 K.Medoids
etc

12 : Visualisation of multidimensional data
12.1 Introduction
12.2 Diagrams for multidimensional visualization
12.3 Visual animation

Index : p. 399 - 403