Model based clustering of multinomial count data

TL;DR

This paper introduces a comprehensive model-based clustering approach for multinomial count data, utilizing both ML and Bayesian methods, with algorithms and model selection criteria, demonstrated on simulated and real datasets.

Contribution

It presents novel EM and MCMC algorithms for clustering multinomial data, along with model selection strategies, and provides an R package for implementation.

Findings

Effective clustering demonstrated on simulated data

Successful application to real datasets

Available R package for practical use

Abstract

We consider the problem of inferring an unknown number of clusters in replicated multinomial data. Under a model based clustering point of view, this task can be treated by estimating finite mixtures of multinomial distributions with or without covariates. Both Maximum Likelihood (ML) as well as Bayesian estimation are taken into account. Under a Maximum Likelihood approach, we provide an Expectation--Maximization (EM) algorithm which exploits a careful initialization procedure combined with a ridge--stabilized implementation of the Newton--Raphson method in the M--step. Under a Bayesian setup, a stochastic gradient Markov chain Monte Carlo (MCMC) algorithm embedded within a prior parallel tempering scheme is devised. The number of clusters is selected according to the Integrated Completed Likelihood criterion in the ML approach and estimating the number of non-empty components in overfitting mixture models in the Bayesian case. Our method is illustrated in simulated data and applied to two real datasets. An R package is available at https://github.com/mqbssppe/multinomialLogitMix.