Improved Clustering Algorithm Based on Calculus of Variation

Authors:
Benson S. Y. Lam;Hong Yan
Affiliations:
City University of Hong Kong, Kowloon, Hong Kong;University of Sydney, NSW 2006, Australia
Venue:
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Year:
2006

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A Regularized Clustering Algorithm Based on Calculus of Variations

Journal of Signal Processing Systems

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Abstract

A major problem in data clustering is the degradation in performance due to outliers. We have developed a robust method to solve this problem using the l2m-FCM algorithm. However, this method has to solve a non-linear equation and can converge to a local optimum. In this paper, we introduce a regularized version of the l2m-FCM algorithm. The essential idea is to constrain the descent direction in the optimization procedure. We employ a novel method to correct the direction using the calculus of variations. Experimental results show that the proposed method has a better performance than seven other clustering algorithms for both synthetic and real world data sets.