A Regularized Clustering Algorithm Based on Calculus of Variations

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.