Data Mining Methods for Knowledge Discovery / Edition 1

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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.
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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.
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Product Details

Table of Contents

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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