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Introduction

Rapid advances in data collection and storage technology have enabled or

ganizations to accumulate vast amounts of data. However, extracting useful

information has proven extremely challenging. Often, traditional data analy

sis tools and techniques cannot be used because of the massive size of a data

set. Sometimes, the non-traditional nature of the data means that traditional

approaches cannot be applied even if the data set is relatively small. In other

situations, the questions that need to be answered cannot be addressed using

existing data analysis techniques, and thus, new methods need to be devel

oped.

Data mining is a technology that blends traditional data analysis methods

with sophisticated algorithms for processing large volumes of data. It has also

opened up exciting opportunities for exploring and analyzing new types of

data and for analyzing old types of data in new ways. In this introductory

chapter, we present an overview of data mining and outline the key topics

to be covered in this book. We start with a description of some well-known

applications that require new techniques for data analysis.

Business Point-of-sale data collection (bar code scanners, radio frequency

identification (RFID), and smart card technology) have allowed retailers to

collect up-to-the-minute data about customer purchases at the checkout coun

ters of their stores. Retailers can utilize this information, along with other

business-critical data such as Web logs from e-commerce Web sites and cus

tomer service records from call centers, to help them better understand the

needs of their customers and make more informed business decisions.

Data mining techniques can be used to support a wide range of business

intelligence applications such as customer profiling, targeted marketing, work

flow management, store layout, and fraud detection. It can also help retailers

Pang-Ning Tan现为密歇根州立大学计算机与工程系助理教授，主要教授数据挖掘、数据库系统等课程。此前，他曾是明尼苏达大学美国陆军高性能计算研究中心副研究员（2002-2003）。

Michael Steinbach 明尼苏达大学计算机与工程系研究员，在读博士。

Vipin Kumar明尼苏达大学计算机科学与工程系主任，曾任美国陆军高性能计算研究中心主任。他拥有马里兰大学博士学位，是数据挖掘和高性能计算方面的国际权威，IEEE会士。

Chapter 1. Introduction
Pang-Ning Tan/Michael Steinbach/Vipin Kumar 1
Chapter 2. Data
Pang-Ning Tan/Michael Steinbach/Vipin Kumar 19
Chapter 3. Exploring Data
Pang-Ning Tan/Michael Steinbach/Vipin Kumar 97
Chapter 4. Classification: Basic Concepts, Decision Trees, and Model Evaluation
Pang-Ning Tan/Michael Steinbach/Vipin Kumar 145
Chapter 5. Classification: Alternative Techniques
Pang-Ning Tan/Michael Steinbach/Vipin Kumar 207
Chapter 6. Association Analysis: Basic Concepts and Algorithms
Pang-Ning Tan/Michael Steinbach/Vipin Kumar 327
Chapter 7. Association Analysis: Advanced Concepts
Pang-Ning Tan/Michael Steinbach/Vipin Kumar 415
Chapter 8. Cluster Analysis: Basic Concepts and Algorithms
Pang-Ning Tan/Michael Steinbach/Vipin Kumar 487
Chapter 9. Cluster Analysis: Additional Issues and Algorithms
Pang-Ning Tan/Michael Steinbach/Vipin Kumar 569
Chapter 10. Anomaly Detection
Pang-Ning Tan/Michael Steinbach/Vipin Kumar 651
Appendix B: Dimensionality Reduction
Pang-Ning Tan/Michael Steinbach/Vipin Kumar 685
Appendix D: Regression
Pang-Ning Tan/Michael Steinbach/Vipin Kumar 703
Appendix E: Optimization
Pang-Ning Tan/Michael Steinbach/Vipin Kumar 713
Copyright Permissions
Pang-Ning Tan/Michael Steinbach/Vipin Kumar 724
Index 725
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