Data Mining_ Concepts and Techniques - Jiawei Han [8]
■ You should have some programming experience. In particular, you should be able to read pseudocode and understand simple data structures such as multidimensional arrays.
To the Professional
This book was designed to cover a wide range of topics in the data mining field. As a result, it is an excellent handbook on the subject. Because each chapter is designed to be as standalone as possible, you can focus on the topics that most interest you. The book can be used by application programmers and information service managers who wish to learn about the key ideas of data mining on their own. The book would also be useful for technical data analysis staff in banking, insurance, medicine, and retailing industries who are interested in applying data mining solutions to their businesses. Moreover, the book may serve as a comprehensive survey of the data mining field, which may also benefit researchers who would like to advance the state-of-the-art in data mining and extend the scope of data mining applications.
The techniques and algorithms presented are of practical utility. Rather than selecting algorithms that perform well on small “toy” data sets, the algorithms described in the book are geared for the discovery of patterns and knowledge hidden in large, real data sets. Algorithms presented in the book are illustrated in pseudocode. The pseudocode is similar to the C programming language, yet is designed so that it should be easy to 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 of your choice to be a fairly straightforward task.
Book Web Sites with Resources
The book has a web site at www.cs.uiuc.edu/~hanj/bk3 and another with Morgan Kaufmann Publishers at
www.booksite.mkp.com/datamining3e. These web sites contain many supplemental materials for readers of this book or anyone else with an interest in data mining. The resources include the following:
■ Slide presentations for each chapter. Lecture notes in Microsoft PowerPoint slides are available for each chapter.
■ Companion chapters on advanced data mining. Chapter 8, Chapter 9 and Chapter 10 of the second edition of the book, which cover mining complex data types, are available on the book's web sites for readers who are interested in learning more about such advanced topics, beyond the themes covered in this book.
■ Instructors' manual. This complete set of answers to the exercises in the book is available only to instructors from the publisher's web site.
■ Course syllabi and lecture plans. These are given for undergraduate and graduate versions of introductory and advanced courses on data mining, which use the text and slides.
■ Supplemental reading lists with hyperlinks. Seminal papers for supplemental reading are organized per chapter.
■ Links to data mining data sets and software. We provide a set of links to data mining data sets and sites that contain interesting data mining software packages, such as IlliMine from the University of Illinois at Urbana-Champaign http://illimine.cs.uiuc.edu.
■ Sample assignments, exams, and course projects. A set of sample assignments, exams, and course projects is available to instructors from the publisher's web site.
■ Figures from the book. This may help you to make your own slides for your classroom teaching.
■ Contents of the book in PDF format.
■ Errata on the different printings of the book. We encourage you to point out any errors in this book. Once the error is confirmed, we will update the errata list and include acknowledgment of your contribution.
Comments or suggestions can be sent to hanj@cs.uiuc.edu. We would be happy to hear from you.
Acknowledgments
Third Edition of the Book
We would like to express our grateful thanks to all of the previous and current members of the Data Mining Group at UIUC, the faculty and students in the Data and