Data Mining Methods for Knowledge Discovery

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Overview

Data Mining Methods for Knowledge Discovery provides an introduction to the data mining methods that are frequently used in the process of knowledge discovery. Numerous illustrative examples and experimental findings are also included. Each chapter comes with an extensive bibliography. Data Mining Methods for Knowledge Discovery is intended for senior undergraduate and graduate students, as well as a broad audience of professionals in computer and information sciences, medical informatics, and business information systems.

Editorial Reviews

Booknews

Introduces data mining methods that are frequently used in the process of knowledge discovery. First elaborates the fundamentals of data mining methods, including rough sets, Bayesian analysis, fuzzy sets, genetic algorithms, machine learning, neural networks, and preprocessing techniques. Then discusses these methods in the setting of the overall process of knowledge discovery. Numerous illustrative examples and experimental findings are included. For students and professionals in computer and information sciences, medical informatics, and business information systems. Annotation c. by Book News, Inc., Portland, Or.

Product Details

ISBN-13: 9780792382522
Publisher: Springer US
Publication date: 8/31/1998
Series: The Springer International Series in Engineering and Computer Science , #458
Edition description: 1998
Edition number: 1
Pages: 495
Product dimensions: 9.21 (w) x 6.14 (h) x 1.13 (d)

	Foreword
	Preface
Ch. 1	Data Mining and Knowledge Discovery	1
Ch. 2	Rough Sets	27
Ch. 3	Fuzzy Sets	73
Ch. 4	Bayesian Methods	131
Ch. 5	Evolutionary Computing	193
Ch. 6	Machine Learning	229
Ch. 7	Neural Networks	309
Ch. 8	Clustering	375
Ch. 9	Preprocessing	431
	Index	491

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Data Mining Methods for Knowledge Discovery / Edition 1