Parsimonious Model-Based Clustering with Covariates
Keefe Murphy, Thomas Brendan Murphy · arXiv (Cornell University) · 2017
We consider model-based clustering methods for continuous, correlated data that account for external information available in the presence of mixed-type fixed covariates by proposing the MoEClust suite of models. These allow covariates influence the component weights and/or component densities by modelling the parameters of the mixture as functions of the covariates. A familiar range of constrained eigen-decomposition parameterisations of the component covariance matrices are also accommodated. This paper thus addresses the equivalent aims of including covariates in Gaussian Parsimonious Clustering Models and incorporating parsimonious covariance structures into the Gaussian mixture of experts framework. The MoEClust models demonstrate significant improvement from both perspectives in applications to univariate and multivariate data sets.