A Regularized Clustering Algorithm Based on Calculus of Variations

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Title A Regularized Clustering Algorithm Based on Calculus of Variations
Author LAM, Benson S. Y.; Liew, Alan Wee-Chung; Smith, David K.; Yan, Hong
Journal Name Journal of Signal Processing Systems
Year Published 2008
Place of publication United States
Publisher Springer New York
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 l2m-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.
Peer Reviewed Yes
Published Yes
Alternative URI http://dx.doi.org/10.1007/s11265-007-0119-9
Copyright Statement Copyright 2008 Springer. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. The original publication is available at www.springerlink.com
Volume 50
Page from 281
Page to 292
ISSN 1939-8018
Date Accessioned 2008-05-16
Language en_AU
Research Centre Institute for Integrated and Intelligent Systems
Faculty Faculty of Science, Environment, Engineering and Technology
Subject PRE2009-Pattern Recognition
URI http://hdl.handle.net/10072/21591
Publication Type Journal Articles (Refereed Article)
Publication Type Code c1

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