Multipartition clustering of mixed data with Bayesian networks

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: .

Automated summary

This paper introduces a multipartition clustering method for mixed data, which efficiently handles multifaceted data with several reasonable interpretations by utilizing Bayesian network factorization and the variational Bayes framework.