Data Mining Methods for Knowledge Discovery. Este artículo no está disponible.
Idioma: inglés
Editorial: Springer, 1998
- Primera edición
- Tapa dura
- Usado
Librería: Better World Books: West, Reno, NV, Estados Unidos de AmericaBetter World Books: West
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Pages intact with minimal writing/highlighting. The binding may be loose and creased. Dust jackets/supplements are not included. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.
N° de ref. del artículo GRP82677266
- Título
- Data Mining Methods for Knowledge Discovery
- Autor
- Pedrycz, Witold, Swiniarski, Roman W., Cios, Krzysztof J.
- Editorial
- Springer
- Año de publicación
- 1998
- Estado
- Good
- Encuadernación
- Encuadernación de tapa dura
- Idioma
- inglés
- ISBN 10
- 0792382528
- ISBN 13
- 9780792382522
- Edición
- 1st.
- Peso del artículo
- 4,409 libras
- Dimensiones
- N/A
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.
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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Reseña del editor
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.
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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