A tractable Multi-Partitions Clustering

TL;DR

This paper introduces a new model-based clustering method that partitions variables into multiple independent blocks, enabling simultaneous variable partitioning and clustering, with applications to mixed data and variable selection.

Contribution

The proposed model allows for multiple latent class variables, variable partitioning, and simultaneous estimation, simplifying the clustering process in mixed-data settings.

Findings

Effective variable partitioning into blocks demonstrated on simulated data.

Accurate clustering results shown on real datasets.

Model selection via BIC and MICL improves interpretability.

Abstract

In the framework of model-based clustering, a model allowing several latent class variables is proposed. This model assumes that the distribution of the observed data can be factorized into several independent blocks of variables. Each block is assumed to follow a latent class model ({\it i.e.,} mixture with conditional independence assumption). The proposed model includes variable selection, as a special case, and is able to cope with the mixed-data setting. The simplicity of the model allows to estimate the repartition of the variables into blocks and the mixture parameters simultaneously, thus avoiding to run EM algorithms for each possible repartition of variables into blocks. For the proposed method, a model is defined by the number of blocks, the number of clusters inside each block and the repartition of variables into block. Model selection can be done with two information criteria, the BIC and the MICL, for which an efficient optimization is proposed. The performances of the model are investigated on simulated and real data. It is shown that the proposed method gives a rich interpretation of the dataset at hand ({\it i.e.,} analysis of the repartition of the variables into blocks and analysis of the clusters produced by each block of variables).