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

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Title: mclust 5: Clustering, Classification and Density Estimation Using Gaussian Finite Mixture Models
Authors: Scrucca, Luca
Fop, Michael
Murphy, Thomas Brendan
Raftery, Adrian E.
Permanent link: http://hdl.handle.net/10197/7937
Date: 1-Aug-2016
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.
Funding Details: Science Foundation Ireland
Type of material: Journal Article
Publisher: R Foundation for Statistical Computing
Keywords: Machine learning;Statistics
Language: en
Status of Item: Peer reviewed
Appears in Collections:Mathematics and Statistics Research Collection
Insight Research Collection

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