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A finite mixture model for the unsupervised classification of three-way ordinal data is proposed. Technically, it is a finite mixture of Gaussians observed only through a discretization of its variates. Group specific means and covariances are reparameterized according to parsimonious models. Estimation is carried out through a compositeapproach to reduce the computational burden.