mclust 5: Clustering, Classification and Density Estimation Using Gaussian Finite Mixture Models
|Title:||mclust 5: Clustering, Classification and Density Estimation Using Gaussian Finite Mixture Models||Authors:||Scrucca, Luca
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||Journal:||The R Journal||Volume:||8||Issue:||1||Keywords:||Machine learning; Statistics||Other versions:||https://journal.r-project.org/archive/2016-1/||Language:||en||Status of Item:||Peer reviewed|
|Appears in Collections:||Mathematics and Statistics Research Collection|
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