Data Mining Methods for Knowledge Discovery

Cios, Krzysztof J.; Pedrycz, W.; Swiniarski, Roman W.

ISBN 10: 0792382528 / ISBN 13: 9780792382522

Editore: Kluwer Academic Publishers, 1998

Riguardo questo articolo

Provides an introduction to the data mining methods that are frequently used in the process of knowledge discovery. This book elaborates on the fundamentals of each of the data mining methods. It discusses these methods in the setting of the overall process of knowledge discovery. It is intended for senior undergraduate and graduate students. Series: The Springer International Series in Engineering and Computer Science. Num Pages: 495 pages, biography. BIC Classification: UN. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly; (UU) Undergraduate. Dimension: 241 x 164 x 34. Weight in Grams: 896. . 1998. Hardback. . . . . Books ship from the US and Ireland.

Codice articolo V9780792382522

Dati bibliografici

• Titolo: Data Mining Methods for Knowledge Discovery
• Editore: Kluwer Academic Publishers
• Data di pubblicazione: 1998
• Lingua: Inglese
• ISBN 10: 0792382528
• ISBN 13: 9780792382522
• Rilegatura: Rilegato
• Condizione: New

Descrizione articolo

Riassunto

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.

Contenuti

Foreword. Preface. 1. Data Mining and Knowledge Discovery. 2. Rough Sets. 3. Fuzzy Sets. 4. Bayesian Methods. 5. Evolutionary Computing. 6. Machine Learning. 7. Neural Networks. 8. Clustering. 9. Preprocessing. Index.