A New Fuzzy Clustering Method with Controllable Membership Characteristics
Dian-Rong Yang, Leu-Shing Lan, Shih-Hung Liao · 2006
Clustering is an unsupervised procedure to group objects in accordance with their similarities. For non-separable clusters, the concept of fuzziness is incorporated. Among other approaches, the fuzzy c-means algorithm is the most well-known fuzzy clustering method. In this work, we present a modified form of the fuzzy c-means based on a new definition of distance measure which can be considered as an extension of the conventional one. The key advantage of this new fuzzy clustering scheme is its ability to flexibly control the membership function curves. Analytical formulae have been derived for both cluster centers and the fuzzy partition matrix. Parameter effects related to the membership function curves have also been analyzed. Examples are given to demonstrate the clustering results of the newly presented scheme.