Block clustering with collapsed latent block models

TL;DR

This paper presents a Bayesian extension of the latent block model that automatically determines the number of row and column clusters in data matrices using MCMC, validated on simulated and real datasets.

Contribution

It introduces a Bayesian framework with integrated block parameters and MCMC sampling for unknown cluster numbers, advancing model-based block clustering methods.

Findings

Effective in identifying clusters in simulated data

Validated on real data with promising results

Automatically infers number of clusters

Abstract

We introduce a Bayesian extension of the latent block model for model-based block clustering of data matrices. Our approach considers a block model where block parameters may be integrated out. The result is a posterior defined over the number of clusters in rows and columns and cluster memberships. The number of row and column clusters need not be known in advance as these are sampled along with cluster memberhips using Markov chain Monte Carlo. This differs from existing work on latent block models, where the number of clusters is assumed known or is chosen using some information criteria. We analyze both simulated and real data to validate the technique.