On the selection of m for Fuzzy c-Means

Fuzzy c-means is a well known fuzzy clustering algorithm. It is an unsupervised clustering algorithm that permits us to build a fuzzy partition from data. The algorithm depends on a parameter m which corresponds to the degree of fuzziness of the solution. Large values of m will blur the classes and all elements tend to belong to all clusters. The solutions of the optimization problem depend on the parameter m. That is, different selections of m will typically lead to different partitions. In this paper we study and compare the effect of the selection of m obtained from the fuzzy c-means.