Title: Gaussian Parsimonious Clustering Models with Covariates,
Abstract: 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.