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Title: A new Gaussian mixture model clustering algorithm Authors:  Polychronis Economou - University of Patras (Greece) [presenting]
Abstract: Gaussian mixture models (GMM) are widely used as a probabilistic model for density estimation for multivariate data and as an unsupervised clustering algorithm to provide a soft (fuzzy) clustering to the available data. The GMMs rely on the expectation-maximization algorithm for maximizing the likelihood. A new approach is proposed in the present work, which depends on approximate Bayesian computation and aims not only to estimate the population parameters but also to assign each observation to a specific subpopulation. The performance of the new approach is compared with the expectation-maximization algorithm for the GMM under several challenging simulation problems.