Clustering and variable selection in the presence of mixed variable types and missing data.

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 on the basis of many cognitive and/or behavioral test scores. There are a modest number of patients (486) in the data set along with many (55) test score variables (many of which are discrete valued and/or missing). The goal

SUBMITTER: Storlie CB 

PROVIDER: S-EPMC6240391 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature