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

Authors:
Yau King Lam;Peter W. M. Tsang
Affiliations:
Department of Electronic Engineering, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong;Department of Electronic Engineering, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong
Venue:
Applied Soft Computing
Year:
2012

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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.