Thorough in its coverage from basic to advanced topics, this text presents the algorithms and techniques used in data mining. It introduces readers to various data mining concepts and algorithms.

• A database perspective is used throughout.

  • Provides students with a focused discussion of algorithms, data structures, data types, and complexity of algorithms and space.

• Clearly written algorithms.

  • Enables students to better understand techniques.

• An emphasis on the use of data mining concepts in real-world applications with large database components.

• Appendix providing overview of available data mining products.

• Strategic text organization of four major sections: Introduction, Core Topics, Advanced Topics, and Products.

  • Enables instructors to choose portions of the book to cover in a limited period of time.

I. INTRODUCTION.

II. CORE TOPICS.

III. ADVANCED TOPICS.

IV. APPENDIX.

Data Mining  (Computer Science)

"It is the best book on data mining so far, and I would definitely adopt it for my course. The book is very comprehensive and covers all of the data mining topics and algorithms of which 1 am aware. The depth of coverage of each topic or method is exactly right and appropriate. Each algorithm is presented in pseudocode that is sufficient for any interested readers to convert into a working implementation in a computer language of their choice." — Michael H. Huhns, University of South Carolina

"Discussion on distributed, parallel, and incremental algorithms is outstanding." — Zoran Obradovic, Temple University

Margaret H. Dunham received the B.A. and the M.S. in mathematics from Miami University in Oxford, Ohio. She earned the Ph.D. degree in computer science from Southern Methodist University. Professor Dunham's research interests encompass main memory databases, data mining, temporal databases, and mobile computing. She is currently an Associate Editor for IEEE Transactions on Knowledge and Data Engineering. She has published numerous technical papers in such research areas as database concurrency control and recovery, database machines, main memory databases, and mobile computing.

Margaret Dunham offers the experienced data base professional or graduate level Computer Science student an introduction to the full spectrum of Data Mining concepts and algorithms. Using a database perspective throughout, Professor Dunham examines algorithms, data structures, data types, and complexity of algorithms and space. This text emphasizes the use of data mining concepts in real-world applications with large database components.

• Covers advanced topics such as Web Mining and Spatial/Temporal mining
• Includes succinct coverage of Data Warehousing, OLAP, Multidimensional Data, and Preprocessing
• Provides case studies
• Offers clearly written algorithms to better understand techniques
• Includes a reference on how to use Prototypes and DM products

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