Fully unsupervised fuzzy clustering with entropy criterion

We present a fully unsupervised clustering algorithm in order to overcome the problem of a priori defining the number of clusters. We propose to optimize an objective function which is the sum of two terms. The first one is a generalization of intra-cluster distance within the framework of fuzzy sets. The second one is an entropy term. Our clustering algorithm has been applied to the problem of clustering both remote sensing data and medical images.