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Description

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.

Observation

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.

Contributeurs

Auteur Junjie Wu

Détails sur le produit

DUIN 146HH33NI02

GTIN 9783642298066

Date de publication 10.07.2012

Langues Anglais

Nombre de pages 180

Type de produit Livre

Dimension 235 x 155 x 155  mm

Poids du produit 461 g

Advances in K-means Clustering

A Data Mining Thinking

Junjie Wu

103,91 €

Vendeur: Dodax EU

Date de livraison: entre mardi, 25. décembre et jeudi, 27. décembre

État: Neuf

TVA incluse - Livraison GRATUITE
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103,91 €
TVA incluse - Livraison GRATUITE