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数据挖掘

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Contents xvii

10.1.3 Generalization of Object Identiﬁers and Class/Subclass

Hierarchies 594

10.1.4 Generalization of Class Composition Hierarchies 595

10.1.5 Construction and Mining of Object Cubes 596

10.1.6 Generalization-Based Mining of Plan Databases by

Divide-and-Conquer 596

10.2 Spatial Data Mining 600

10.2.1 Spatial Data Cube Construction and Spatial OLAP 601

10.2.2 Mining Spatial Association and Co-location Patterns 605

10.2.3 Spatial Clustering Methods 606

10.2.4 Spatial Classiﬁcation and Spatial Trend Analysis 606

10.2.5 Mining Raster Databases 607

10.3 Multimedia Data Mining 607

10.3.1 Similarity Search in Multimedia Data 608

10.3.2 Multidimensional Analysis of Multimedia Data 609

10.3.3 Classiﬁcation and Prediction Analysis of Multimedia Data 611

10.3.4 Mining Associations in Multimedia Data 612

10.3.5 Audio and Video Data Mining 613

10.4 Text Mining 614

10.4.1 Text Data Analysis and Information Retrieval 615

10.4.2 Dimensionality Reduction for Text 621

10.4.3 Text Mining Approaches 624

10.5 Mining the World Wide Web 628

10.5.1 Mining the Web Page Layout Structure 630

10.5.2 Mining the Web’s Link Structures to Identify

Authoritative Web Pages 631

10.5.3 Mining Multimedia Data on the Web 637

10.5.4 Automatic Classiﬁcation of Web Documents 638

10.5.5 Web Usage Mining 640

10.6 Summary 641

Exercises 642

Bibliographic Notes 645

Chapter 11 Applications and Trends in Data Mining 649

11.1 Data Mining Applications 649

11.1.1 Data Mining for Financial Data Analysis 649

11.1.2 Data Mining for the Retail Industry 651

11.1.3 Data Mining for the Telecommunication Industry 652

11.1.4 Data Mining for Biological Data Analysis 654

11.1.5 Data Mining in Other Scientiﬁc Applications 657

11.1.6 Data Mining for Intrusion Detection 658

Foreword

We are deluged by data—scientiﬁc data, medical data, demographic data, ﬁnancial data,

and marketing data. People have no time to look at this data. Human attention has

become the precious resource. So, we must ﬁnd ways to automatically analyze the data,

to automatically classify it, to automatically summarize it, to automatically discover and

characterize trends in it, and to automatically ﬂag anomalies. This is one of the most

active and exciting areas of the database research community. Researchers in areas includ-

ing statistics, visualization, artiﬁcial intelligence, and machine learning are contributing

to this ﬁeld. The breadth of the ﬁeld makes it difﬁcult to grasp the extraordinary progress

over the last few decades.

Six years ago, Jiawei Han’s and Micheline Kamber’s seminal textbook organized and

presented Data Mining. It heralded a golden age of innovation in the ﬁeld. This revision

of their book reﬂects that progress; more than half of the references and historical notes

are to recent work. The ﬁeld has matured with many new and improved algorithms, and

has broadened to include many more datatypes: streams, sequences, graphs, time-series,

geospatial, audio, images, and video. We are certainly not at the end of the golden age—

indeed research and commercial interest in data mining continues to grow—but we are

all fortunate to have this modern compendium.

The book gives quick introductions to database and data mining concepts with

particular emphasis on data analysis. It then covers in a chapter-by-chapter tour the con-

cepts and techniques that underlie classiﬁcation, prediction, association, and clustering.

These topics are presented with examples, a tour of the best algorithms for each prob-

lem class, and with pragmatic rules of thumb about when to apply each technique. The

Socratic presentation style is both very readable and very informative. I certainly learned

a lot from reading the ﬁrst edition and got re-educated and updated in reading the second

edition.

Jiawei Han and Micheline Kamber have been leading contributors to data mining

research. This is the text they use with their students to bring them up to speed on the

xix

Preface

Our capabilities of both generating and collecting data have been increasing rapidly.

Contributing factors include the computerization of business, scientiﬁc, and government

transactions; the widespread use of digital cameras, publication tools, and bar codes for

most commercial products; and advances in data collection tools ranging from scanned

text and image platforms to satellite remote sensing systems. In addition, popular use

of the World Wide Web as a global information system has ﬂooded us with a tremen-

dous amount of data and information. This explosive growth in stored or transient data

has generated an urgent need for new techniques and automated tools that can intelli-

gently assist us in transforming the vast amounts of data into useful information and

knowledge.

This book explores the concepts and techniques of data mining, a promising and

ﬂourishing frontier in data and information systems and their applications. Data mining,

also popularly referred to as knowledge discovery from data (KDD), is the automated or

convenient extraction of patterns representing knowledge implicitly stored or captured

in large databases, data warehouses, the Web, other massive information repositories, or

data streams.

Data mining is a multidisciplinary ﬁeld, drawing work from areas including database

technology, machine learning, statistics, pattern recognition, information retrieval,

neural networks, knowledge-based systems, artiﬁcial intelligence, high-performance

computing, and data visualization. We present techniques for the discovery of patterns

hidden in large data sets, focusing on issues relating to their feasibility, usefulness, effec-

tiveness, and scalability. As a result, this book is not intended as an introduction to

database systems, machine learning, statistics, or other such areas, although we do pro-

vide the background necessary in these areas in order to facilitate the reader’s compre-

hension of their respective roles in data mining. Rather, the book is a comprehensive

introduction to data mining, presented with effectiveness and scalability issues in focus.

It should be useful for computing science students, application developers, and business

professionals, as well as researchers involved in any of the disciplines listed above.

Data mining emerged during the late 1980s, made great strides during the 1990s, and

continues to ﬂourish into the new millennium. This book presents an overall picture

of the ﬁeld, introducing interesting data mining techniques and systems and discussing

xxi

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