Advances in K-means Clustering A Data Mining Thinking /

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 establis...

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Détails bibliographiques
Cote:Libro Electrónico
Auteur principal: Wu, Junjie (Auteur)
Collectivité auteur: SpringerLink (Online service)
Format: Électronique eBook
Langue:Inglés
Publié: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2012.
Édition:1st ed. 2012.
Collection:Springer Theses, Recognizing Outstanding Ph.D. Research,
Sujets:
Data mining.
Statistics .
Business information services.
Database management.
Data Mining and Knowledge Discovery.
Statistics in Business, Management, Economics, Finance, Insurance.
IT in Business.
Database Management.
Accès en ligne:Texto Completo
Table des matières:
  • Cluster Analysis and K-means Clustering: An Introduction
  • The Uniform Effect of K-means Clustering
  • Generalizing Distance Functions for Fuzzy c-Means Clustering
  • Information-Theoretic K-means for Text Clustering
  • Selecting External Validation Measures for K-means Clustering
  • K-means Based Local Decomposition for Rare Class Analysis
  • K-means Based Consensus Clustering.

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