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.
Clustering Ordinal Data Via Parsimonious Models / Ranalli, Monia; Rocci, Roberto. - (2024), pp. 380-387. (Intervento presentato al convegno SMPS2024 tenutosi a Salzburg, Austria) [10.1007/978-3-031-65993-5_47].
Clustering Ordinal Data Via Parsimonious Models
Ranalli, Monia;Rocci, Roberto
2024
Abstract
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.| File | Dimensione | Formato | |
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