Unsupervised Non-hierarchical Entropy-based Clustering

Abstract

We present an unsupervised non-hierarchical clustering which realizes a partition of unlabelled objects in K non-overlapping clusters. The interest of this method rests on the convexity of the entropv-based clustering criterion which is demonstrated here. This criterion permits to reach an optimal partition independently of the initial conditions, with a step by step iterative Monte-Carlo process. Several data sets serve to illustrate the main properties of this clustering.