Variable Selection Methods for Model-based Clustering

Abstract

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 model-based 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.

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Cited by in corpus (8)

• Simultaneous Dimension Reduction and Clustering via the NMF-EM Algorithm
• Simultaneous Dimensionality and Complexity Model Selection for Spectral Graph Clustering
• Clustering and Variable Selection in the Presence of Mixed Variable Types and Missing Data
• A sparse negative binomial mixture model for clustering RNA-seq count data
• High-dimensional clustering via Random Projections
• Model based clustering of multinomial count data
• A Bayesian Finite Mixture Model with Variable Selection for Data with Mixed-type Variables
• Model-based Clustering using Automatic Differentiation: Confronting Misspecification and High-Dimensional Data