Junjie Wu: Advances in K-means Clustering

Cover: 9783642447570 | Advances in K-means Clustering | A Data Mining Thinking | Junjie Wu

Rückseite: 9783642447570 | Advances in K-means Clustering | A Data Mining Thinking | Junjie Wu

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Sprache: Englisch

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Beschreibung

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.

Zusammenfassung

Gives an overall picture on how to adapt K-means to the clustering of newly emerging big data

Establishes a theoretical framework for K-means clustering and cluster validity

Studies the dangerous uniform effect and zero-value dilemma of K-means

Demonstrates the novel use of K-means for rare class analysis and consensus clustering

Based on the thesis that won the 2010 National Excellent Doctoral Dissertation Award of China

Includes supplementary material: [...]

Inhaltsverzeichnis

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.

Details

Erscheinungsjahr:	2014
Genre:	Informatik , Mathematik , Medizin , Naturwissenschaften , Technik
Rubrik:	Naturwissenschaften & Technik
Medium:	Taschenbuch
Inhalt:	xvi 180 S.
ISBN-13:	9783642447570
ISBN-10:	3642447570
Sprache:	Englisch
Einband:	Kartoniert / Broschiert
Autor:	Wu, Junjie
Hersteller:	Springer Berlin Springer Berlin Heidelberg
Verantwortliche Person für die EU:	Springer Verlag GmbH, Tiergartenstr. 17, D-69121 Heidelberg, juergen.hartmann@springer.com
Maße:	235 x 155 x 11 mm
Von/Mit:	Junjie Wu
Erscheinungsdatum:	09.08.2014
Gewicht:	0,306 kg

Artikel-ID: 105173604