Title: The mclust R package for clustering, classification and density estimation using Gaussian finite mixture models
Authors: Luca Scrucca - Universita' degli Studi di Perugia (Italy) [presenting]
Michael Fop - University College Dublin (Ireland)
Thomas Brendan Murphy - University College Dublin (Ireland)
Adrian E Raftery - University of Washington (United States)
Abstract: Finite mixture models are being used increasingly to model a wide variety of random phenomena for clustering, classification and density estimation. mclust is a powerful and popular R package implementing Gaussian finite mixtures with different covariance structures and different numbers of mixture components. An integrated approach is provided, with functions that combine model-based hierarchical clustering, EM for mixture estimation and several tools for model selection. Recent updates have introduced new covariance structures, dimension reduction capabilities for visualisation, model selection criteria, initialisation strategies for the EM algorithm, and bootstrap-based inference, making it a full-featured R package for data analysis via finite mixture modelling.