Introduction to Data Mining: Pearson New International Edition

 

1 Introduction

1.1 What is Data Mining?

1.2 Motivating Challenges

1.3 The Origins of Data Mining

1.4 Data Mining Tasks

1.5 Scope and Organization of the Book 

1.6 Bibliographic Notes

1.7 Exercises

 

2 Data

2.1 Types of Data

2.2 Data Quality

2.3 Data Preprocessing

2.4 Measures of Similarity and Dissimilarity

2.5 Bibliographic Notes

2.6 Exercises

 

3 Exploring Data

3.1 The Iris Data Set 

3.2 Summary Statistics

3.3 Visualization

3.4 OLAP and Multidimensional Data Analysis

3.5 Bibliographic Notes

3.6 Exercises

 

4 Classification: Basic Concepts, Decision Trees, and Model Evaluation

4.1 Preliminaries

4.2 General Approach to Solving a Classification Problem

4.3 Decision Tree Induction

4.4 Model Overfitting

4.5 Evaluating the Performance of a Classifier

4.6 Methods for Comparing Classifiers

4.7 Bibliographic Notes

4.8 Exercises

 

5 Classification: Alternative Techniques

5.1 Rule-Based Classifier

5.2 Nearest-Neighbor Classifiers

5.3 Bayesian Classifiers

5.4 Artificial Neural Network (ANN)

5.5 Support Vector Machine (SVM)

5.6 Ensemble Methods

5.7 Class Imbalance Problem

5.8 Multiclass Problem

5.9 Bibliographic Notes

5.10 Exercises

 

6 Association Analysis: Basic Concepts and Algorithms

6.1 Problem Definition

6.2 Frequent Itemset Generation

6.3 Rule Generation

6.4 Compact Representation of Frequent Itemsets

6.5 Alternative Methods for Generating Frequent Itemsets

6.6 FP-Growth Algorithm

6.7 Evaluation of Association Patterns

6.8 Effect of Skewed Support Distribution

6.9 Bibliographic Notes

6.10 Exercises



   

9 Cluster Analysis: Basic Concepts and Algorithms

8.1 Overview

8.2 K-means

8.3 Agglomerative Hierarchical Clustering

8.4 DBSCAN

8.5 Cluster Evaluation

8.6 Bibliographic Notes

8.7 Exercises

 

10 Cluster Analysis: Additional Issues and Algorithms

9.1 Characteristics of Data, Clusters, and Clustering Algorithms

9.2 Prototype-Based Clustering

9.3 Density-Based Clustering

9.4 Graph-Based Clustering

9.5 Scalable Clustering Algorithms

9.6 Which Clustering Algorithm?

9.7 Bibliographic Notes

9.8 Exercises

 

11 Anomaly Detection

10.1 Preliminaries

10.2 Statistical Approaches

10.3 Proximity-Based Outlier Detection

10.4 Density-Based Outlier Detection

10.5 Clustering-Based Techniques

10.6 Bibliographic Notes

10.7 Exercises

 

Appendix B Dimensionality Reduction

Appendix D Regression

Appendix E Optimization

 

Author Index

Subject Index