Data Mining Methods for Knowledge Discovery
By: by Krzysztof J. Cios, Witold Pedrycz, Roman W. Swiniarski
Contributors: Pedrycz, Witold; Swiniarski, Roman W.
Series: The Springer International Series in Engineering and Computer Science -- 458; International series in engineering and computer science -- 458.
Publisher: Springer US
Published: 1998
Place of publication: Boston, MA
Language: eng
Format: [electronic resource] /
Number of pages: 495
ISBN-13: 9781461375579; 9781461555896
Subjects: Information storage and retrieval systems; Data structures (Computer science); Computer science; Artificial intelligence; Management information systems
Description: Data Mining Methods for Knowledge Discovery provides an introduction to the data mining methods that are frequently used in the process of knowledge discovery. This book first elaborates on the fundamentals of each of the data mining methods: rough sets, Bayesian analysis, fuzzy sets, genetic algorithms, machine learning, neural networks, and preprocessing techniques. The book then goes on to thoroughly discuss these methods in the setting of the overall 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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