Fuzzy c-means is a well known fuzzy clustering algorithm. It is an unsupervised clustering algorithmthat permits us to build a fuzzy partition from data. The algorithm depends on a parameter m whichcorresponds to the degree of fuzziness of the solution. Large values of m will blur the classes andall elements tend to belong to all clusters. The solutionsof the optimization problem depend on theparameter m. That is, different selections of m willtypically lead to different partitions. In this paper we study and compare the effect ofthe selection of m obtained from the fuzzy c-means.
Conference general chair: Luis Magdalena Layos