Clustering by maximizing a fuzzy classification maximum likelihood criterion
Christophe Ambroise, Gérard Govaert · COMPSTAT · 2000
Basing cluster analysis on mixture models has become a classical and powerful approach. In this paper we propose an extension of this approach to fuzzy clustering. We define a fuzzy clustering criterion which generalizes both the maximum likelihood and the classification maximum likelihood. Finally using a generalization of the well-known EM and CEM algorithms we design an algorithm to optimize this criterion.