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Titlebook: Classification and Clustering for Knowledge Discovery; Saman Halgamuge,Lipo Wang Book 2005 Springer-Verlag Berlin Heidelberg 2005 Extensio

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書目名稱Classification and Clustering for Knowledge Discovery
編輯Saman Halgamuge,Lipo Wang
視頻videohttp://file.papertrans.cn/228/227194/227194.mp4
概述Includes supplementary material:
叢書名稱Studies in Computational Intelligence
圖書封面Titlebook: Classification and Clustering for Knowledge Discovery;  Saman Halgamuge,Lipo Wang Book 2005 Springer-Verlag Berlin Heidelberg 2005 Extensio
描述.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. .
出版日期Book 2005
關(guān)鍵詞Extension; classification; clustering; communication; computational intelligence; data analysis; decision
版次1
doihttps://doi.org/10.1007/b98152
isbn_softcover978-3-642-06542-2
isbn_ebook978-3-540-32404-1Series ISSN 1860-949X Series E-ISSN 1860-9503
issn_series 1860-949X
copyrightSpringer-Verlag Berlin Heidelberg 2005
The information of publication is updating

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https://doi.org/10.1007/978-981-99-8982-9enance, adaptability and flexibility, (2) Discourse semantics (explanatory capabilities) – provision for explanation and justifying outputs given by the expert advisory system, (3) Metaconsequent – mapping final aggregated output from a fuzzy If–Then rule onto a finite database, and (4) Deployment of large scale advisory expert system.
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D-GridMST: Clustering Large Distributed Spatial Databases,w space requirement and small network transferring overhead. Experimental results show that D–GridMST is effective since it is able to produce exactly the same clustering result as that produced in the centralized paradigm, making D-GridMST a promising tool for clustering large distributed spatial databases.
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P-Expert: Implementation and Deployment of Large Scale Fuzzy Expert Advisory System,enance, adaptability and flexibility, (2) Discourse semantics (explanatory capabilities) – provision for explanation and justifying outputs given by the expert advisory system, (3) Metaconsequent – mapping final aggregated output from a fuzzy If–Then rule onto a finite database, and (4) Deployment of large scale advisory expert system.
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