An improved fuzzy c-means algorithm for unbalanced sized clusters

Abstract

In this paper, we propose an improved fuzzy c-means (FCM) algorithm based on cluster height information to deal with the sensitivity of unbalanced sized clusters in FCM. As we know, cluster size sensitivity is an major drawback of FCM, which tends to balance the cluster sizes during iteration, so the center of smaller cluster might be drawn to the adjacent larger one, which will lead to bad classification. To overcome this problem, the cluster height information is considered and introduced to the distance function to adjust the conventional Euclidean distance, thus to control the effect on classification from cluster size difference. Experimental results demonstrate that our algorithm can obtain good clustering results in spite of great size difference, while traditional FCM cannot work well in such case. The improved FCM has shown its potential for extracting small clusters, especially in medical image segmentation.