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

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

Finite mixture models are being used increasingly to model a wide variety of random phenomena for clustering, classification and density estimation. is a powerful and popular package which allows modelling of data as a Gaussian finite mixture with different covariance structures and different number...

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Veröffentlicht in:The R journal 2016-08, Vol.8 (1), p.289-317
Hauptverfasser: Scrucca, Luca, Fop, Michael, Murphy, T Brendan, Raftery, Adrian E
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Fop, Michael
Murphy, T Brendan
Raftery, Adrian E
description Finite mixture models are being used increasingly to model a wide variety of random phenomena for clustering, classification and density estimation. 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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title mclust 5: Clustering, Classification and Density Estimation Using Gaussian Finite Mixture Models
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