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Variable selection methods for model-based clustering


Michael Fop, Brendan Murphy

Publication Type: 
Refereed Original Article
Model-based clustering is a popular approach for clustering multivariate data which has seen applications in numerous fields. Nowadays, high-dimensional data are more and more common and the modelbased clustering approach has adapted to deal with the increasing dimensionality. In particular, the development of variable selection techniques has received a lot of attention and research effort in recent years. Even for small size problems, variable selection has been advocated to facilitate the interpretation of the clustering results. This review provides a summary of the methods developed for variable selection in model-based clustering. Existing R packages implementing the different methods are indicated and illustrated in application to two data analysis examples
Digital Object Identifer (DOI): 
Publication Status: 
Date Accepted for Publication: 
Monday, 14 May, 2018
Publication Date: 
Statistics Surveys
National University of Ireland, Dublin (UCD)
Open access repository: