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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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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 : A Data Mining Thinking
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Springer Berlin Heidelberg, 2012
ISBN 10: 3642298060
ISBN 13: 9783642298066
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Gebunden. Condizione: 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. Codice articolo 5056029
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Buch. Condizione: 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. Codice articolo 9783642298066
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Advances in K-Means Clustering : A Data Mining Thinking
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Advances in K-means Clustering : A Data Mining Thinking
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Buch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. Codice articolo 9783642298066
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Advances in K-means Clustering
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Buch. Condizione: 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. Codice articolo 9783642298066
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Advances in K-means Clustering: A Data Mining Thinking (Springer Theses: Recognizing Outstanding Ph.D. Research)
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Advances in K-means Clustering
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Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, 2012
ISBN 10: 3642298060
ISBN 13: 9783642298066
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Condizione: New. The K-means algorithm is commonly used in data mining and business intelligence. This award-winning research pioneers its application to the intricacies of 'big data', detailing a theoretical framework for aggregating and validating clusters with K-means. Series: Springer Theses. Num Pages: 196 pages, biography. BIC Classification: KJQ; PBT; UNF. Category: (P) Professional & Vocational. Dimension: 235 x 155 x 13. Weight in Grams: 461. . 2012. Hardback. . . . . Codice articolo V9783642298066
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