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Advances in K-means Clustering: A Data Mining Thinking (Springer Theses: Recognizing Outstanding Ph.D. Research) - Hardcover
Synopsis
Nearly everyone knows K-means algorithm in the fields of data mining and business intelligence. But the ever-emerging data with extremely complicated characteristics bring new challenges to this "old" algorithm. This book addresses these challenges and makes novel contributions in establishing theoretical frameworks for K-means distances and K-means based consensus clustering, identifying the "dangerous" uniform effect and zero-value dilemma of K-means, adapting right measures for cluster validity, and integrating K-means with SVMs for rare class analysis. This book not only enriches the clustering and optimization theories, but also provides good guidance for the practical use of K-means, especially for important tasks such as network intrusion detection and credit fraud prediction. The thesis on which this book is based has won the "2010 National Excellent Doctoral Dissertation Award", the highest honor for not more than 100 PhD theses per year in China.
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Nearly everyone knows K-means algorithm in the fields of data mining and business intelligence. But the ever-emerging data with extremely complicated characteristics bring new challenges to this "old" algorithm. This book addresses these challenges and makes novel contributions in establishing theoretical frameworks for K-means distances and K-means based consensus clustering, identifying the "dangerous" uniform effect and zero-value dilemma of K-means, adapting right measures for cluster validity, and integrating K-means with SVMs for rare class analysis. This book not only enriches the clustering and optimization theories, but also provides good guidance for the practical use of K-means, especially for important tasks such as network intrusion detection and credit fraud prediction. The thesis on which this book is based has won the "2010 National Excellent Doctoral Dissertation Award", the highest honor for not more than 100 PhD theses per year in China.
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Advances in K-means Clustering
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Springer Berlin Heidelberg, Springer Berlin Heidelberg Jul 2012, 2012
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Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Nearly everyone knows K-means algorithm in the fields of data mining and business intelligence. But the ever-emerging data with extremely complicated characteristics bring new challenges to this 'old' algorithm. This book addresses these challenges and makes novel contributions in establishing theoretical frameworks for K-means distances and K-means based consensus clustering, identifying the 'dangerous' uniform effect and zero-value dilemma of K-means, adapting right measures for cluster validity, and integrating K-means with SVMs for rare class analysis. This book not only enriches the clustering and optimization theories, but also provides good guidance for the practical use of K-means, especially for important tasks such as network intrusion detection and credit fraud prediction. The thesis on which this book is based has won the '2010 National Excellent Doctoral Dissertation Award', the highest honor for not more than 100 PhD theses per year in China. 196 pp. Englisch. Seller Inventory # 9783642298066
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Advances in K-Means Clustering : A Data Mining Thinking
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Advances in K-means Clustering
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Gebunden. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Gives an overall picture on how to adapt K-means to the clustering of newly emerging big dataEstablishes a theoretical framework for K-means clustering and cluster validityStudies the dangerous uniform effect and zero-value dilemma of K-mea. Seller Inventory # 5056029
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Advances in K-means Clustering
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ISBN 10: 3642298060
ISBN 13: 9783642298066
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Buch. Condition: Neu. Neuware -Nearly everyone knows K-means algorithm in the fields of data mining and business intelligence. But the ever-emerging data with extremely complicated characteristics bring new challenges to this 'old' algorithm. This book addresses these challenges and makes novel contributions in establishing theoretical frameworks for K-means distances and K-means based consensus clustering, identifying the 'dangerous' uniform effect and zero-value dilemma of K-means, adapting right measures for cluster validity, and integrating K-means with SVMs for rare class analysis. This book not only enriches the clustering and optimization theories, but also provides good guidance for the practical use of K-means, especially for important tasks such as network intrusion detection and credit fraud prediction. The thesis on which this book is based has won the '2010 National Excellent Doctoral Dissertation Award', the highest honor for not more than 100 PhD theses per year in China.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 196 pp. Englisch. Seller Inventory # 9783642298066
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