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A classification EM algorithm for clustering and two stochastic versions. (English) Zbl 0937.62605
Summary: 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.

MSC:
62H30 Classification and discrimination; cluster analysis (statistical aspects)
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