Improved Linear Fuzzy Clustering Considering Compactness and Separation

Abstract

Fuzzy Compactness and Separation (FCS) is an extension of Fuzzy c-Means (FCM) for finding compact but separate clusters with the combined objective function of the FCM aggregation criterion and the cluster separation measure. This paper further extends FCS to linear fuzzy clustering by replaceing FCM prototypes with lines such that the clustering criterion of Fuzzy c-Lines (FCL) is combined with the degree of cluster distortion measuring the distances among each data object and the global centroid. Experimental results demonstrate that the initialization sensitivity can be improved by introducing the separation criterion in linear fuzzy clustering.