Published by the Foundation for Open Access Statistics
Editors-in-chief: Bettina Grün, Torsten Hothorn, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
clustvarsel: A Package Implementing Variable Selection for Gaussian Model-Based Clustering in R | Scrucca | Journal of Statistical Software
Authors: Luca Scrucca, Adrian E. Raftery
Title: clustvarsel: A Package Implementing Variable Selection for Gaussian Model-Based Clustering in R
Abstract: Finite mixture modeling provides a framework for cluster analysis based on parsimonious Gaussian mixture models. Variable or feature selection is of particular importance in situations where only a subset of the available variables provide clustering information. This enables the selection of a more parsimonious model, yielding more efficient estimates, a clearer interpretation and, often, improved clustering partitions. This paper describes the R package clustvarsel which performs subset selection for model-based clustering. An improved version of the Raftery and Dean (2006) methodology is implemented in the new release of the package to find the (locally) optimal subset of variables with group/cluster information in a dataset. Search over the solution space is performed using either a stepwise greedy search or a headlong algorithm. Adjustments for speeding up these algorithms are discussed, as well as a parallel implementation of the stepwise search. Usage of the package is presented through the discussion of several data examples.

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Paper: clustvarsel: A Package Implementing Variable Selection for Gaussian Model-Based Clustering in R     Download PDF (Downloads: 939)
Supplements:
clustvarsel_2.3.2.tar.gz: R source package Download (Downloads: 51; 26KB)
v84i01.R: R replication code Download (Downloads: 68; 25KB)

DOI: 10.18637/jss.v084.i01

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.