“mclust 5: Clustering, Classification and Density Estimation Using Gaussian Finite Mixture Models” (2016), by Luca Scrucca et al. Computer Science research paper. Original abstract below, unchanged. No free PDF was found.

mclust 5: Clustering, Classification and Density Estimation Using Gaussian Finite Mixture Models

Luca Scrucca, Michael Fop, Thomas Brendan Murphy et al. · January 1, 2016 · Computer Science

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 package which allows modelling of data as a Gaussian finite mixture with different covariance structures and different numbers of mixture components, for a variety of purposes of analysis.Recently, version 5 of the package has been made available on CRAN.This updated version adds 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.

Source page DOI: 10.32614/rj-2016-021