Multipartition clustering of mixed data with Bayesian networks

International Journal of Intelligent SystemsVolume 37, Issue 3 p. 2188-2218 RESEARCH ARTICLE Multipartition clustering of mixed data with Bayesian networks Fernando Rodriguez-Sanchez, Corresponding Author Fernando Rodriguez-Sanchez [email protected] orcid.org/0000-0003-0790-6983 Computational Intelligence Group, Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, Madrid, Spain Correspondence Fernando Rodriguez-Sanchez, Computational Intelligence Group, Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, Madrid, Spain. Email: [email protected]Search for more papers by this authorConcha Bielza, Concha Bielza orcid.org/0000-0001-7109-2668 Computational Intelligence Group, Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, Madrid, SpainSearch for more papers by this authorPedro Larrañaga, Pedro Larrañaga orcid.org/0000-0002-1885-4501 Computational Intelligence Group, Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, Madrid, SpainSearch for more papers by this author Fernando Rodriguez-Sanchez, Corresponding Author Fernando Rodriguez-Sanchez [email protected] orcid.org/0000-0003-0790-6983 Computational Intelligence Group, Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, Madrid, Spain Correspondence Fernando Rodriguez-Sanchez, Computational Intelligence Group, Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, Madrid, Spain. Email: [email protected]Search for more papers by this authorConcha Bielza, Concha Bielza orcid.org/0000-0001-7109-2668 Computational Intelligence Group, Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, Madrid, SpainSearch for more papers by this authorPedro Larrañaga, Pedro Larrañaga orcid.org/0000-0002-1885-4501 Computational Intelligence Group, Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, Madrid, SpainSearch for more papers by this author First published: 30 November 2021 https://doi.org/10.1002/int.22770Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Abstract Real-world applications often involve multifaceted data with several reasonable interpretations. To cluster this data, we need methods that are able to produce multiple clustering solutions. To this purpose, it is interesting to learn a finite mixture model with multiple latent variables, where each latent variable represents a unique way to partition the data. However, although there is an extensive literature on multipartition clustering methods for categorical data and for continuous data, there is a lack of work for mixed data. In this paper, we propose a multipartition clustering method that is able to efficiently deal with mixed data by exploiting the Bayesian network factorization and the variational Bayes framework. We show the flexibility and applicability of the proposed method by solving clustering, density estimation, and missing data imputation tasks in real-world data sets. For reproducibility, all code, data, and results can be found in the following public repository: https://github.com/ferjorosa/mpc-mixed. Supporting Information Filename Description int22770-sup-0001-updated_supporting_information.pdf459.1 KB Supplementary Information Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article. Volume37, Issue3March 2022Pages 2188-2218 RelatedInformation