Journal
BIOINFORMATICS
Volume 35, Issue 7, Pages 1255-1257Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/bty786
Keywords
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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.
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