A CLASSIFICATION EM ALGORITHM FOR CLUSTERING AND 2 STOCHASTIC VERSIONS

Setting the optimization-based clustering methods under the classification maximum likelihood approach, we define and study a general Classification EM algorithm. Then, we derive from this algorithm two stochastic algorithms, incorporating random perturbations, to reduce the initial-position dependence of the classical optimization clustering algorithms. Numerical experiments, reported for the variance criterion, show that both stochastic algorithms perform well compared with the standard k-means algorithm which is a particular version of the Classification EM algorithm.