There have been rapid developments in model-based clustering of graphs, also known as
block modelling, over the last ten years or so. We review different approaches and …

Cluster analysis finds groups in data automatically. Most methods have been heuristic and
leave open such central questions as: how many clusters are there? Which method should I …

ABSTRACT A natural Bayesian approach for mixture models with an unknown number of
components is to take the usual finite mixture model with symmetric Dirichlet weights, and …

Cluster analysis seeks to identify homogeneous subgroups of cases in a population. This
article provides an introduction to model‐based clustering using finite mixture models and …

This article establishes a general formulation for Bayesian model-based clustering, in which
subset labels are exchangeable, and items are also exchangeable, possibly up to covariate …

For data assumed to come from a finite mixture with an unknown number of components, it
has become common to use Dirichlet process mixtures (DPMs) not only for density …

Identifying the number of classes in Bayesian finite mixture models is a challenging problem.
Several criteria have been proposed, such as adaptations of the deviance information …

This paper deals with estimation and model selection in the Latent Block Model (LBM) for
categorical data. First, after providing sufficient conditions ensuring the identifiability of this …

The stochastic block model (SBM) is a mixture model for the clustering of nodes in networks.
The SBM has now been employed for more than a decade to analyze very different types of …

This chapter presents some of the Bayesian solutions to the different interpretations of
picking the “right” number of components in a mixture, before concluding on the ill-posed …