A clustering algorithm for overlapping Gaussian mixtures
Polychronis Εconomou · Research in Statistics · 2023
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 clustering to the available data.The GMM relies 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, in terms of mean estimates for the parameters of each subpopulation, number of observations assigned to each cluster, accuracy, and other performance metrics, of the new approach is compared with the expectation-maximization algorithm for the GMM and the K-means algorithm under several challenging simulation scenarios and is applied to a real data set.