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- Leidėjas: Springer-Verlag GmbH
- Metai: 2005
- ISBN-10: 3540260730
- ISBN-13: 9783540260738
- Formatas: 16.7 x 24.4 x 2.5 cm, kieti viršeliai
- Kalba: Anglų
- Extra -25 % nuolaida šiai knygai su kodu: ENG25
Classification and Clustering for Knowledge Discovery (el. knyga) (skaityta knyga) | knygos.lt
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Aprašymas
Knowledge Discovery today is a significant study and research area. In finding answers to many research questions in this area, the ultimate hope is that knowledge can be extracted from various forms of data around us. This book covers recent advances in unsupervised and supervised data analysis methods in Computational Intelligence for knowledge discovery. In its first part the book provides a collection of recent research on distributed clustering, self organizing maps and their recent extensions. If labeled data or data with known associations are available, we may be able to use supervised data analysis methods, such as classifying neural networks, fuzzy rule-based classifiers, and decision trees. Therefore this book presents a collection of important methods of supervised data analysis. "Classification and Clustering for Knowledge Discovery" also includes variety of applications of knowledge discovery in health, safety, commerce, mechatronics, sensor networks, and telecommunications.
EXTRA 25 % nuolaida su kodu: ENG25
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- Leidėjas: Springer-Verlag GmbH
- Metai: 2005
- ISBN-10: 3540260730
- ISBN-13: 9783540260738
- Formatas: 16.7 x 24.4 x 2.5 cm, kieti viršeliai
- Kalba: Anglų Anglų
Knowledge Discovery today is a significant study and research area. In finding answers to many research questions in this area, the ultimate hope is that knowledge can be extracted from various forms of data around us. This book covers recent advances in unsupervised and supervised data analysis methods in Computational Intelligence for knowledge discovery. In its first part the book provides a collection of recent research on distributed clustering, self organizing maps and their recent extensions. If labeled data or data with known associations are available, we may be able to use supervised data analysis methods, such as classifying neural networks, fuzzy rule-based classifiers, and decision trees. Therefore this book presents a collection of important methods of supervised data analysis. "Classification and Clustering for Knowledge Discovery" also includes variety of applications of knowledge discovery in health, safety, commerce, mechatronics, sensor networks, and telecommunications.
Atsiliepimai