clustvarsel: A Package Implementing Variable Selection for Gaussian Model-Based Clustering in R

Luca Scrucca, Adrian E. Raftery
2018 Journal of Statistical Software  
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
more » ... ion 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 step-wise 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.
doi:10.18637/jss.v084.i01 pmid:30450020 pmcid:PMC6238955 fatcat:2v6ypaetgvdfxjxwobsp2iqsxu