Data Mining Methods for Knowledge Discovery (The Springer International Series in Engineering and Computer Science, 458)

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

ISBN 10: 0792382528 / ISBN 13: 9780792382522

Editore: Springer, 1998

Riguardo questo articolo

In shrink wrap. Looks like an interesting title!

Codice articolo Q-0792382528

Dati bibliografici

• Titolo: Data Mining Methods for Knowledge Discovery (The Springer International Series in Engineering and Computer Science, 458)
• Editore: Springer
• Data di pubblicazione: 1998
• Lingua: Inglese
• ISBN 10: 0792382528
• ISBN 13: 9780792382522
• Rilegatura: hardcover
• Condizione: New
• Peso: 71 once

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.