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.

References in zbMATH (referenced in 16 articles , 1 standard article )

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  1. Marbac, Matthieu; Sedki, Mohammed; Patin, Tienne: Variable selection for mixed data clustering: application in human population genomics (2020)
  2. Celeux, Gilles; Maugis-Rabusseau, Cathy; Sedki, Mohammed: Variable selection in model-based clustering and discriminant analysis with a regularization approach (2019)
  3. Crook, Oliver M.; Gatto, Laurent; Kirk, Paul D. W.: Fast approximate inference for variable selection in Dirichlet process mixtures, with an application to pan-cancer proteomics (2019)
  4. Fop, Michael; Murphy, Thomas Brendan: Variable selection methods for model-based clustering (2018)
  5. Galimberti, Giuliano; Manisi, Annamaria; Soffritti, Gabriele: Modelling the role of variables in model-based cluster analysis (2018)
  6. Luca Scrucca; Adrian Raftery: clustvarsel: A Package Implementing Variable Selection for Gaussian Model-Based Clustering in R (2018) not zbMATH
  7. Wallace, Meredith L.; Buysse, Daniel J.; Germain, Anne; Hall, Martica H.; Iyengar, Satish: Variable selection for skewed model-based clustering: application to the identification of novel sleep phenotypes (2018)
  8. Marbac, Matthieu; Sedki, Mohammed: Variable selection for model-based clustering using the integrated complete-data likelihood (2017)
  9. McNicholas, Paul D.: Model-based clustering (2016)
  10. Morris, Katherine; McNicholas, Paul D.: Clustering, classification, discriminant analysis, and dimension reduction via generalized hyperbolic mixtures (2016)
  11. Andrews, Jeffrey L.; McNicholas, Paul D.: Variable selection for clustering and classification (2014)
  12. Morris, Katherine; McNicholas, Paul D.; Scrucca, Luca: Dimension reduction for model-based clustering via mixtures of multivariate (t)-distributions (2013)
  13. Vahid Nia; Anthony Davison: High-Dimensional Bayesian Clustering with Variable Selection: The R Package bclust (2012) not zbMATH
  14. Andrews, Jeffrey L.; Mcnicholas, Paul D.: Extending mixtures of multivariate (t)-factor analyzers (2011)
  15. Andrews, Jeffrey L.; McNicholas, Paul D.: Extending mixtures of multivariate (t)-factor analyzers (2011)
  16. 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)