The MinMax k-Means clustering algorithm

Applying k-Means to minimize the sum of the intra-cluster variances is the most popular clustering approach. However, after a bad initialization, poor local optima can be easily obtained. To tackle the initialization problem of k-Means, we propose the MinMax k-Means algorithm, a method that assigns...

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Bibliographic Details
Published in:	Pattern recognition Vol. 47; no. 7; pp. 2505 - 2516
Main Authors:	Tzortzis, Grigorios, Likas, Aristidis
Format:	Journal Article
Language:	English
Published:	Kidlington Elsevier Ltd 01.07.2014 Elsevier
Subjects:	Algorithms Applied sciences Balanced clusters Cluster analysis Clustering Clusters Emergence Exact sciences and technology Information, signal and communications theory k-Means k-Means initialization Pattern recognition Robustness Signal and communications theory Signal representation. Spectral analysis Signal, noise Telecommunications and information theory Variance Weighting k-Means initialization Balanced clusters Clustering k-Means Performance evaluation Automatic classification Weighting Minimax method Iterative method K means algorithm Robustness Signal classification
ISSN:	0031-3203, 1873-5142
Online Access:	Get full text
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