Generalized fuzzy c-means clustering strategies using L/sub p/ norm distances

Abstract

Fuzzy c-means (FCM) is a useful clustering technique. Modifications of FCM using L/sub 1/ norm distances increase robustness to outliers. Object and relational data versions of FCM clustering are defined for the more general case where the L/sub p/ norm (p/spl ges/1) or semi-norm (0<p<1) is used as the measure of dissimilarity. We give simple (though computationally intensive) alternating optimization schemes for all object data cases of p>0 in order to facilitate the empirical examination of the object data models. Both object and relational approaches are included in a numerical study.