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

“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.

- Authors: Luca Scrucca, Michael Fop, Thomas Brendan Murphy, Adrian,E. Raftery
- Published: January 1, 2016
- Field: Computer Science
- DOI: https://doi.org/10.32614/rj-2016-021
- Source: https://doi.org/10.32614/rj-2016-021

## 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.

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