Clustering performance of different density function weighted FCM algorithm

Abstract

Fuzzy C-Means (FCM) algorithm is an unsupervised fuzzy clustering method. Clustering results accuracy of the algorithm is affected by equal partition trend of the data sets. When amount of each cluster sample are difference greatly, the optimal solution of the algorithm may not be the correct partition of the data sets. Weighted Fuzzy C-Means (WFCM) algorithm is proposed to overcome this disadvantage. The WFCM algorithm contained a density function which calculates density of each sample by Gaussian function or reciprocal of distance function. The density function solves the problem of equal partition trend to some extent, and also retains favorable convergence and stability for the FCM algorithm. The experiment results are evaluated by the cluster indexes, such as partition coefficient, partition entropy and Xie-Beni index. It shows which weighted function improves the clustering performance of the WFCM algorithm better. When partially supervised information obtained from a few labeled samples is introduced to the WFCM algorithm, the clustering performance of the WFCM algorithm is further enhanced and the convergent speed of objective function is further accelerated.