Data Mining: A Tutorial Based Primer (絕)
- 20本以上，享 8.5折
售價
$
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- 一般書籍
- ISBN：9780321223494
- 作者：Richard Roiger, Michael Geatz
- 版次：1
- 年份：2003
- 出版商：Pearson Education
書籍介紹
本書特色
目錄
作者介紹
Description
This primer on data mining provides an introduction to the principles and techniques for extracting information from a business-minded perspective. A basic familiarity with the field of data mining concepts is built and then enhanced via 13 data mining tutorials. Upon completion of these tutorials, students will be fully able to data mine. This book is appropriate for students of CS, MIS, and Information Technology.
This primer on data mining provides an introduction to the principles and techniques for extracting information from a business-minded perspective. A basic familiarity with the field of data mining concepts is built and then enhanced via 13 data mining tutorials. Upon completion of these tutorials, students will be fully able to data mine. This book is appropriate for students of CS, MIS, and Information Technology.
Features
- Has a learn by doing approach to teaching the basic theory and how-to of data mining.
- Includes numerous Data Mining Sessions (step by step instructions) throughout the chapters.
- The software that accompanies the book (an Excel based add-on) gives first hand experience with the data mining process.
- Numerous business, science and health data sets are introduced and used in the text. The actual data sets are included in the accompanying CDROM.
- End of chapter material includes basic chapter review questions, more data mining projects, and theoretical based questions.
- Includes introduction to closely related topics: data warehouse design, rule based expert systems, intelligent agents
Table of Contents
Preface.
I. DATA MINING FUNDAMENTALS.
1. Data Mining: A First View.
2. Data Mining: A Closer Look.
3. Basic Data Mining Techniques.
4. An Excel-Based Data Mining Tool.
II. TOOLS FOR KNOWLEDGE DISCOVERY.
5. Knowledge Discovery in Databases.
6. The Data Warehouse.
7. Formal Evaluation Techniques.
III. ADVANCED DATA MINING TECHNIQUES.
8. Neural Networks.
9. Building Neural Networks with iDA.
10. Statistical Techniques.
11. Specialized Techniques.
IV. INTELLIGENT SYSTEMS.
12. Rule-Based Systems.
13. Managing Uncertainty in Rule-Based Systems.
14. Intelligent Agents.
Appendix.
Preface.
I. DATA MINING FUNDAMENTALS.
1. Data Mining: A First View.
2. Data Mining: A Closer Look.
3. Basic Data Mining Techniques.
4. An Excel-Based Data Mining Tool.
II. TOOLS FOR KNOWLEDGE DISCOVERY.
5. Knowledge Discovery in Databases.
6. The Data Warehouse.
7. Formal Evaluation Techniques.
III. ADVANCED DATA MINING TECHNIQUES.
8. Neural Networks.
9. Building Neural Networks with iDA.
10. Statistical Techniques.
11. Specialized Techniques.
IV. INTELLIGENT SYSTEMS.
12. Rule-Based Systems.
13. Managing Uncertainty in Rule-Based Systems.
14. Intelligent Agents.
Appendix.
Richard J. Roiger is a professor of computer science at Minnesota State University, Mankato and a senior software engineer for Information Acumen Corporation (www.infoacumen.com). Richard received a Ph.D. degree in Computer Science from the University of Minnesota in 1991. His research interests include machine learning, knowledge discovery in databases and expert systems. He is a member of the American Association of Artificial Intelligence, the Association for Computing Machinery and IEEE. He is also a musician and songwriter, enjoys spending time with his children and grandchildren and likes playing golf as time permits.
