Improved Fuzzy C-Means Clustering Algorithm

Fuzzy C-Means cluster algorithm is the most widespread and sensitive in fuzzy clustering analysis. However its shortcoming is the sensibility to initial value, it usually leads to local minimum. And this algorithm requires the user to predefine the number of clusters (c); however, it is not always possible to know c in advance.Aiming at above problem, this paper presents the global Fuzzy C-Means clustering algorithm which is an incremental approach to clustering. It does not depend on any initial conditions and the better clustering results are obtained through a deterministic global search procedure. The paper also proposes the fast global Fuzzy C-Means clustering algorithm to improve the converging speed of the global Fuzzy C-Means clustering algorithm. Experiments show that the global Fuzzy C-Means clustering algorithm can give us more satisfactory results by escaping from the sensibility to initial value and improving the accuracy of clustering; the fast global Fuzzy C-Means clustering algorithm improved the converging speed of the global Fuzzy C-Means clustering algorithm without significantly affecting solution quality.Furthermore, for the problem that the number of clusters is not known a priori, a new cluster validity index is proposed. The proposed validity index exploits a variation measure and a separation measure between fuzzy clusters. The variation measure, which indicates the degree of scatter of the data within a cluster, is obtained by computing the intra cluster errors. The separation measure, which indicates the isolation distance between fuzzy clusters, is obtained by computing a distance between fuzzy clusters. A good fuzzy partition is expected to have a low degree of variation and a large separation distance. Experimental results showed the superior effectiveness and reliability of the proposed index.