clustvarsel: A Package Implementing Variable Selection for Model-based Clustering in R
Abstract
Finite mixture modelling 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 methodology of Raftery and Dean (2006) is implemented in the new version 2 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.
Cite
@article{arxiv.1411.0606,
title = {clustvarsel: A Package Implementing Variable Selection for Model-based Clustering in R},
author = {Luca Scrucca and Adrian E. Raftery},
journal= {arXiv preprint arXiv:1411.0606},
year = {2014}
}