R package clustvarsel: Variable Selection for Model-Based Clustering. A function which implements variable selection methodology for model-based clustering which allows to find the (locally) optimal subset of variables in a dataset that have group/cluster information. A greedy or headlong search can be used, either in a forward-backward or backward-forward direction, with or without sub-sampling at the hierarchical clustering stage for starting Mclust models. By default the algorithm uses a sequential search, but parallelization is also available.
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References in zbMATH (referenced in 8 articles )
Showing results 1 to 8 of 8.
- Galimberti, Giuliano; Manisi, Annamaria; Soffritti, Gabriele: Modelling the role of variables in model-based cluster analysis (2018)
- Marbac, Matthieu; Sedki, Mohammed: Variable selection for model-based clustering using the integrated complete-data likelihood (2017)
- McNicholas, Paul D.: Model-based clustering (2016)
- Andrews, Jeffrey L.; McNicholas, Paul D.: Variable selection for clustering and classification (2014)
- Morris, Katherine; McNicholas, Paul D.; Scrucca, Luca: Dimension reduction for model-based clustering via mixtures of multivariate $t$-distributions (2013)
- Andrews, Jeffrey L.; Mcnicholas, Paul D.: Extending mixtures of multivariate $t$-factor analyzers (2011)
- Andrews, Jeffrey L.; McNicholas, Paul D.: Extending mixtures of multivariate $t$-factor analyzers (2011)
- McNicholas, P.D.; Murphy, T.B.; McDaid, A.F.; Frost, D.: Serial and parallel implementations of model-based clustering via parsimonious Gaussian mixture models (2010)