Model-Based Clustering and Variable Selection for Multivariate Count Data

Julien Jacques, Thomas Brendan Murphy 2025-07-01

Citation

Julien Jacques and Thomas Brendan Murphy (July 2025). Model-Based Clustering and Variable Selection for Multivariate Count Data. Computo. https://doi.org/10.57750/6v7b-8483

Badges

Authors’ affiliations

• Julien Jacques (Université Lumière Lyon 2, Universite Claude Bernard Lyon 1, ERIC, 69007, Lyon, France)
• Thomas Brendan Murphy (School of Mathematics & Statistics, University College Dublin, Institut d’Études Avancées, Université de Lyon)

Abstract

Model-based clustering provides a principled way of developing clustering methods. We develop a new model-based clustering methods for count data. The method combines clustering and variable selection for improved clustering. The method is based on conditionally independent Poisson mixture models and Poisson generalized linear models. The method is demonstrated on simulated data and data from an ultra running race, where the method yields excellent clustering and variable selection performance.