On the selection of m for Fuzzy c-Means
Vicenç Torra · Advances in intelligent systems research/Advances in Intelligent Systems Research · 2015
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.