334 JournalofProteom e Research • Vol. 2, No. 3, 2003 Data Mining: Concepts, Models, Methods, and Algorithms
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Bog'liqData Mining Concepts Models Methods and
© 2003 American Chemical Society books in review 334 JournalofProteom e Research • Vol. 2, No. 3, 2003 Data Mining: Concepts, Models, Methods, and Algorithms By Mehmed Kantardzic Wiley-IEEE Press:Totowa, NJ, 2003, 343 pp ISBN 0-471-22852-4 $59.95 W ritten by a well-established expert in computer science and data-min- ing methods, Data Mining: Concepts, Mod- els, Methods, and Algorithms provides an introduction to the concepts, widely used algorithms, and visualization methods used to extract information from databases. The book is designed to provide supporting material for undergraduate and graduate classes and includes review questions and problems at the end of each chapter. The book also serves as an excellent starting point for anyone wishing to learn about data mining. Each chapter starts with a set of objectives and an introduction and ends with relevant references. The book is divided into 12 chapters and 2 appendices. Chapter 1 introduces the data-mining process—including data col- lection, preprocessing, and analysis—and discusses the important concept of a data warehouse. Chapter 2 describes the prin- ciples of data representation, techniques for data preparation, and methods for addressing incomplete datasets. The final section, which describes outlier analysis, is particularly useful. Chapter 3 explains the advantages of data reduction as part of preprocessing the data and describes the methods that can be applied. Chapter 4 then covers the gen- eral theoretical background of data mining and discusses the philosophy of learning from data. Chapters 5 through 11, however, con- stitute the real meat of the book, covering the various algorithmic approaches to data mining. Chapters 5 and 6 cover standard statistical and clustering methods; chap- ters 7 and 8 deal with decision trees and association rules; and chapters 9, 10, and 11 describe artificial neural networks, genetic algorithms, and fuzzy logic. Each method and its fundamentals are well introduced and illustrated with appropri- ate examples. Then the underlying math- ematical model is described in more depth. The final chapter deals with the per- ception and visualization of data, both as a method of data mining and as a means to interpret its results. The book provides useful supporting material in the form of a comprehensive survey of available data- mining tools in Appendix A and a com- plete set of references. Finally, Appendix B details examples of the application of data-mining methods to real-world prob- lems from a wide variety of industry sec- tors such as finance, retail, engineering, and biomedical research. Overall, this is a comprehensive text- book that describes the process and methodologies of data mining in an unbi- ased manner. It provides a well-laid-out discussion of the choice of data-mining method as an iterative procedure that allows one to explore the data as part of an effort to elucidate the underlying patterns hidden within it. Readers who seek to understand not only data-mining concepts but also the mathematical models that can be applied will find this book particularly useful. DAVID EDWARDS Director, Computational Proteomics, Accelrys, Inc. BOOKS RECEIVED Download 177.33 Kb. Do'stlaringiz bilan baham: |
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