Articles liés à Advances in K-means Clustering: A Data Mining Thinking
Synopsis
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.
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Advances in K-Means Clustering : A Data Mining Thinking
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Advances in K-means Clustering (Hardcover)
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Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, Berlin, 2012
ISBN 10 : 3642298060
ISBN 13 : 9783642298066
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Hardcover. Etat : new. Hardcover. 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. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 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 : A Data Mining Thinking
Edité par
Springer, 2012
ISBN 10 : 3642298060
ISBN 13 : 9783642298066
Ancien ou d'occasion
Couverture rigide
Vendeur : GreatBookPrices, Columbia, MD, Etats-Unis
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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 : A Data Mining Thinking
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Advances in K-means Clustering
Edité par
Springer Berlin Heidelberg, Springer Berlin Heidelberg Jul 2012, 2012
ISBN 10 : 3642298060
ISBN 13 : 9783642298066
Neuf
Couverture rigide
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
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Buch. Etat : 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. N° de réf. du vendeur 9783642298066
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Advances in K-means Clustering
Edité par
Springer Berlin Heidelberg, 2012
ISBN 10 : 3642298060
ISBN 13 : 9783642298066
Neuf
Couverture rigide
Vendeur : moluna, Greven, Allemagne
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Gebunden. Etat : 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. N° de réf. du vendeur 5056029
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Advances in K-Means Clustering : A Data Mining Thinking
Edité par
Springer, 2012
ISBN 10 : 3642298060
ISBN 13 : 9783642298066
Ancien ou d'occasion
Couverture rigide
Vendeur : GreatBookPricesUK, Woodford Green, Royaume-Uni
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Etat : As New. Unread book in perfect condition. N° de réf. du vendeur 17845286
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Advances in K-means Clustering
Edité par
Springer Berlin Heidelberg, Springer Berlin Heidelberg Jul 2012, 2012
ISBN 10 : 3642298060
ISBN 13 : 9783642298066
Neuf
Couverture rigide
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Buch. Etat : 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. N° de réf. du vendeur 9783642298066
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