Bias-correction fuzzy clustering algorithms

Abstract

Fuzzy clustering is generally an extension of hard clustering and it is based on fuzzy membership partitions. In fuzzy clustering, the fuzzy c-means (FCM) algorithm is the most commonly used clustering method. Numerous studies have presented various generalizations of the FCM algorithm. However, the FCM algorithm and its generalizations are usually affected by initializations. In this paper, we propose a bias-correction term with an updating equation to adjust the effects of initializations on fuzzy clustering algorithms. We first propose the so-called bias-correction fuzzy clustering of the generalized FCM algorithm. We then construct the bias-correction FCM, bias-correction Gustafson and Kessel clustering and bias-correction inter-cluster separation algorithms. We compared the proposed bias-correction fuzzy clustering algorithms with other fuzzy clustering algorithms by using numerical examples. We also applied the bias-correction fuzzy clustering algorithms to real data sets. The results indicated the superiority and effectiveness of the proposed bias-correction fuzzy clustering methods.