EXploratory K-Means : A new simple and efficient algorithm for gene clustering

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

29 Scopus Citations
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Detail(s)

Original languageEnglish
Pages (from-to)1149-1157
Journal / PublicationApplied Soft Computing Journal
Volume12
Issue number3
Publication statusPublished - Mar 2012

Abstract

In this paper, a novel gene expression clustering method known as eXploratory K-Means (XK-Means) is proposed. The method is based on the integration of the K-Means framework, and an exploratory mechanism to prevent premature convergence of the clustering process. Experimental results reveal that the performance of XK-Means in grouping gene expressions, measured in terms of speed, error and stability, is superior to existing methods that are based on evolutionary algorithm. In addition, the complexity of the proposed method is lower and the method can be easily implemented in practice. © 2011 Elsevier B.V. All rights reserved.

Research Area(s)

  • Bioinformatics, Computational biology, eXploratory K-Means, Gene clustering, K-Means, Particle Swarm Optimization