Variable selection methods for model-based clustering

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Title: Variable selection methods for model-based clustering
Authors: Fop, MichaelMurphy, Thomas Brendan
Permanent link: http://hdl.handle.net/10197/10520
Date: 26-Apr-2018
Online since: 2019-05-20T09:23:26Z
Abstract: Model-based clustering is a popular approach for clustering multivariate data which has seen applications in numerous fields. Nowadays, high-dimensional data are more and more common and the model-based clustering approach has adapted to deal with the increasing dimensionality. In particular, the development of variable selection techniques has received a lot of attention and research effort in recent years. Even for small size problems, variable selection has been advocated to facilitate the interpretation of the clustering results. This review provides a summary of the methods developed for variable selection in model-based clustering. Existing R packages implementing the different methods are indicated and illustrated in application to two data analysis examples.
Funding Details: Science Foundation Ireland
Type of material: Journal Article
Publisher: The American Statistical Association, the Bernoulli Society, the Institute of Mathematical Statistics, and the Statistical Society of Canada
Journal: Statistics Surveys
Volume: 12
Start page: 18
End page: 65
Copyright (published version): 2018 the Authors
Keywords: Gaussian mixture modelLatent class analysisModel-based clusteringR packagesVariable selection
DOI: 10.1214/18-SS119
Language: en
Status of Item: Peer reviewed
Appears in Collections:Insight Research Collection
Insight Research Collection

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