ENGLISH

Data Mining Methods for Knowledge Discovery

By Witold Pedrycz , Krzysztof J. Cios , Roman W. Swiniarski (auth.)

Book information

Publisher
Springer US
Year
1998
ISBN
978-1-4613-7557-9, 978-1-4615-5589-6
DOI
10.1007/978-1-4615-5589-6
Language
english
Format
PDF
Filesize
34 MB (35979450 bytes)
Series
The Springer International Series in Engineering and Computer Science 458
Edition
1
Pages
495\507
Orientation
yes
Scanned
yes
Time added
2013-08-01 04:00:00
Cryptology and Information Theory; Information StoData Structures

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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