Matthieu Marbac1, Mohammed Sedki2. 1. CREST, Ensai, Bruz, France. 2. University of Paris-Sud and UMR Inserm-1181, Paris, France.
Abstract
SUMMARY: VarSelLCM allows a full model selection (detection of the relevant features for clustering and selection of the number of clusters) in model-based clustering, according to classical information criteria. Data to be analyzed can be composed of continuous, integer and/or categorical features. Moreover, missing values are managed, without any pre-processing, by the model used to cluster with the assumption that values are missing completely at random. Thus, VarSelLCM also allows data imputation by using mixture models. A Shiny application is implemented to easily interpret the clustering results. AVAILABILITY AND IMPLEMENTATION: VarSelLCM is available to download at https://CRAN.R-project.org/package=VarSelLCM/. TUTORIAL: vignette is available online at http://varsellcm.r-forge.r-project.org/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
SUMMARY: VarSelLCM allows a full model selection (detection of the relevant features for clustering and selection of the number of clusters) in model-based clustering, according to classical information criteria. Data to be analyzed can be composed of continuous, integer and/or categorical features. Moreover, missing values are managed, without any pre-processing, by the model used to cluster with the assumption that values are missing completely at random. Thus, VarSelLCM also allows data imputation by using mixture models. A Shiny application is implemented to easily interpret the clustering results. AVAILABILITY AND IMPLEMENTATION: VarSelLCM is available to download at https://CRAN.R-project.org/package=VarSelLCM/. TUTORIAL: vignette is available online at http://varsellcm.r-forge.r-project.org/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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