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来自「This complete matlab for neural network」· 文本 代码 · 共 106 行

发信人: ccipt (北方的狼), 信区: DataMining
标  题: Preface
发信站: 南京大学小百合站 (Wed Aug 22 10:55:53 2001)


Preface
　　Our capabilities of both generating and collecting data have been increasi
ng rap idly in the last several decades. Contributing factors include the wide
spread us e of bar codes for most commercial products, the computerization of 
many business, scientific, and government transactions, and advances in data c
ollection tool s ranging from scanned text and image platforms to satellite re
mote sensing systems. In addition, popular use of the World Wide Web as a glob
al information system has flooded us with a tremendous amount of data and info
rmation. This explosive growth in stored data has generated an urgent need for
 new techniques and automated tools that can intelligently assist us in transf
orming 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 database systems and new database applications. D
ata mining, also popularly referred to as knowledge discovery in databases (KD
D), is the automated or convenient extraction of patterns representing knowled
ge implicitly stored in large databases, data warehouses, and other massive in
formation repositories.
　　Data mining is a multidisciplinary field, drawing work from areas includin
g data base technology, artificial intelligence, machine learning, neural netw
orks, statistics, pattern recognition, knowledge-based systems, knowledge acqu
isition, in formation retrieval, high-performance computing, and data visualiz
ation. We present the material in this book from a database perspective. That 
is, we focus on issues relating to the feasibility, usefulness, efficiency, an
d scalability of techniques for the discovery of patterns hidden in large data
bases. as a result, this book is not intended as an introduction to database s
ystems, 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 comprehension of their respective roles in data mining. Rather, the book is
 a comprehensive introduction to data mining, presented with database issues i
n focus. It should be useful for computing science students, application developers, and business professionals, as well as researchers invol
ved in any of the disciplines listed above.
　　Data mining emerged during the late 1980s, has made great strides during t
he 199 0s, and is expected to continue to flourish into the new millennium. Th
is book p resents an overall picture of the field from a database researcher's
 point of view, introducing interesting data mining techniques and systems, an
d discussing applications and research directions. An important motivation for
 writing this boo k was the need to build an organized framework for the study
 of data mining—a challenging task owing to the extensive multidisciplinary n
ature of this fast developing field. We hope that this book will encourage peo
ple with different backgrounds and experiences to exchange their views regardi
ng data mining so as to contribute toward the further promotion and shaping of
 this exciting and dynamic field.
To the Teacher
　　This book is designed to give a broad, yet in-depth overview of the field 
of data mining. You will find it useful for teaching a course on data mining a
t an advanced undergraduate level or the first-year graduate level. In additio
n, individual chapters may be included as material for courses on selected top
ics in database systems or in artificial intelligence. We have tried to make t
he chapters as self-contained as possible so that you are not confined to read
ing each chapter in sequence. For a course taught at the undergraduate level, 
you might use Chapters 1 through 8 as the core course material. Remaining clas
s material may be selected from among the more advanced topics described in Ch
apters 9 and 10. For a graduate-level course, you may choose to cover the enti
re book in one semester.
　　Each chapter ends with a set of exercises, suitable as assigned homework. 
The exercises are either short questions that text basic mastery of the materi
al covered, or longer questions that require analytical thinking.
To the Student
　　We hope that this textbook will spark your interest in the fresh, yet evol
ving field of data mining. We have attempted to present the material in a clea
r manner, with careful explanation of the topics covered. Each chapter ends wi
th a summary describing the main points. We have included many figures and ill
ustrations throughout the text in order to make the book more enjoyable and “
reader-friendly”. Although this book was designed as a textbook, we have trie
d to organize it so that it will also be useful to you as a reference book or 
handbook, should you later decide to pursue a career in data mining.
　　What do you need to know in order to read this book?
　　·You should have some knowledge of the concepts and terminololgy associat
ed with database systems. However, we do try to provide enough background of t
he basics in database technology, so that if your memory is a bit rusty, you w
ill not have trouble following the discussions in the book. You should have so
me knowledge of database querying, although knowledge of any specific query la
nguage is not required.
　　·You should have some programming experience. In particular, you should b
e able t o read pseudocode, and understand simple data structures such as mult
idimensional arrays.
　　·It will be helpful to have some preliminary background in statistics, ma
chine learning, or pattern recognition. However, we will familiarize you with 
the basic concepts of these areas that are relevant to data mining from a data
base perspective.
To the Professional
　　This book was designed to cover a broad range of topics in the field of da
ta mining. As a result, it is an excellent handbook on the subject. Because ea
ch chapter is designed to be as stand-alone as possible, you can focus on the 
topics that most interest you. Much of the book is suited to applications prog
rammers or information service managers like yourself who with to learn about 
the key ideas of data mining on their own.
　　The techniques and algorithms presented are of practical utility. Rather t
han selecting algorithms that perform well on small “toy”databases, the algo
rithms described in the book are geared for the discovery of data patterns hid
den in large, real databases. In Chapter 10, we briefly discuss data mining sy
stems in commercial use, as well as promising research prototypes. Each algori
thm presented in the book is illustrated in pseudocode. The pseudocode is simi
lar to the C programming language, yet is designed so that it should be easy t
o follow by programmers unfamiliar with C or C++. If you wish to implement any
 of the algorithms, you should find the translation of our pseudocode into the
 programming language o f your choice to be a fairly straightforward task.
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