Clustering and Variable Selection in the Presence of Mixed Variable Types and Missing Data

TL;DR

This paper introduces a model-based clustering method for mixed continuous and discrete variables with missing data, incorporating variable selection to identify key features influencing cluster formation, demonstrated on autism spectrum disorder data.

Contribution

It presents a novel approach combining Dirichlet process mixture models with variable selection for mixed data with missing values, applicable to health sciences and beyond.

Findings

Successfully identified three clusters in ASD data

Selected four test scores as most informative for clustering

Outperformed existing methods in simulation studies

Abstract

We consider the problem of model-based clustering in the presence of many correlated, mixed continuous and discrete variables, some of which may have missing values. Discrete variables are treated with a latent continuous variable approach and the Dirichlet process is used to construct a mixture model with an unknown number of components. Variable selection is also performed to identify the variables that are most influential for determining cluster membership. The work is motivated by the need to cluster patients thought to potentially have autism spectrum disorder (ASD) on the basis of many cognitive and/or behavioral test scores. There are a modest number of patients (~480) in the data set along with many (~100) test score variables (many of which are discrete valued and/or missing). The goal of the work is to (i) cluster these patients into similar groups to help identify those with similar clinical presentation, and (ii) identify a sparse subset of tests that inform the clusters in order to eliminate unnecessary testing. The proposed approach compares very favorably to other methods via simulation of problems of this type. The results of the ASD analysis suggested three clusters to be most likely, while only four test scores had high (>0.5) posterior probability of being informative. This will result in much more efficient and informative testing. The need to cluster observations on the basis of many correlated, continuous/discrete variables with missing values, is a common problem in the health sciences as well as in many other disciplines.