Biclustering of discrete data by extended finite mixtures of latent trait models

We aim at performing a joint clustering of units and variables in a binary data matrix in a biclustering perspective. In this framework, units are partitioned into clusters (components) via a finite mixture approach; in each component, variables are partitioned into clusters (segments) by adopting a flexible specification of the linear predictor. Dependence between variables is modeled via a multidimensional, continuous, latent trait. The proposed model is applied to the Regensburg Pediatric Appendicitis data set, with the aim of identifying homogeneous groups of pediatric patients with respect to subsets of clinical features.