A Regularized Clustering Algorithm Based on Calculus of Variations

Authors:
Benson S. Lam;Alan Wee-Chung Liew;David K. Smith;Hong Yan
Affiliations:
Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong;School of Information and Communication Technology, Griffith University, Brisbane, Australia;Department of Biochemistry, University of Hong Kong, Pok Fu Lam, Hong Kong;Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong and School of Electrical and Information Engineering, University of Sydney, Sydney, Australia 2006
Venue:
Journal of Signal Processing Systems
Year:
2008

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Abstract

Microarray data clustering has drawn great attention in recent years. However, a major problem in data clustering is convergence to a local optimal solution. In this paper, we introduce a regularized version of the l 2m-FCM algorithm to resolve this problem. The strategy is to constrain the descent direction in the optimization procedure. For this we employ a novel method, calculus of variations, to correct the direction. Experimental results show that the proposed method has a better performance than seven other clustering algorithms for three synthetic and six real world data sets. Also, the proposed method produces reliable results for synthetic data sets with a large number of groups, which is a challenging problem for many clustering algorithms. Our method has been applied to microarray data classification with good results.