CONTENTS

1. Introduction to Data Mining

1.1 Introduction

1.2 Data Mining Tasks

1.3 DM Versus Knowledge Discovery in Databases

1.4 Data Mining Issues

1.5 Data Mining Metrics

1.6 Social Implications of Data Mining

1.7 Overview of Applications of Data Mining

2. Introduction to Data Warehousing

2.1 Introduction

2.2 Architecture of Data warehouse

2.3 OLAP and data cubes

2.4 Dimensional Data Modeling

2.5 Data Preprocessing

2.6 Machine learning

2.7 Pattern Matching

3. Data Mining Techniques

3.1 Introduction

3.2  Frequent item-set and Association rule mining

3.3 Graph Mining

4. Classification and Prediction

4.1 Introduction

4.2 Decision Tree learning

4.3 Bayesian classification

4.4  Linear classification

4.5 prediction

5. Accuracy Measures

5.1 Introduction

5.2 Precision

5.3 Recall

5.4 F-measure

5.5 Confusion matrix

5.6 Cross-validation

5.7 Bootstrap

6. Software for Data Mining and Applications of Data Mining

6.1 Introduction

6.2 R

6.3 Weka

6.4  Sample applications of data mining

7. Clustering

7.1 Introduction

7.2 k-means

7.3 Expectation Maximization (EM) algorithm

7.4 Hierarchical clustering

7.5 Correlation clustering

8. Brief Overview of Advanced Techniques

8.1 Introduction

8.2 Active Learning

8.3 Reinforcement learning

8.4 Text mining

8.5 Graphical model

8.6 Web Mining