Clustering Ordinal Data Via Parsimonious Models

This review presents some parsimonious models to cluster two-way and three-way ordinal data. They are formulated has a reparameterization of a finite mixture of Gaussians that is partially observed through a discretization of its variates. Model parameters are estimated using a composite likelihood approach in order to reduce the numerical complexity. The parsimony is obtained by reducing the dimensionality of the variable’s space within and/or between the components.