Mixed Deep Gaussian Mixture Model: a clustering model for mixed datasets

Robin Fuchs (), Denys Pommeret () and Cinzia Viroli ()
Additional contact information
Robin Fuchs: Aix-Marseille University
Denys Pommeret: Univ Lyon, UCBL, ISFA LSAF EA2429
Cinzia Viroli: University of Bologna

Advances in Data Analysis and Classification, 2022, vol. 16, issue 1, No 3, 53 pages

Abstract: Abstract Clustering mixed data presents numerous challenges inherent to the very heterogeneous nature of the variables. A clustering algorithm should be able, despite this heterogeneity, to extract discriminant pieces of information from the variables in order to design groups. In this work we introduce a multilayer architecture model-based clustering method called Mixed Deep Gaussian Mixture Model that can be viewed as an automatic way to merge the clustering performed separately on continuous and non-continuous data. This architecture is flexible and can be adapted to mixed as well as to continuous or non-continuous data. In this sense, we generalize Generalized Linear Latent Variable Models and Deep Gaussian Mixture Models. We also design a new initialisation strategy and a data-driven method that selects the best specification of the model and the optimal number of clusters for a given dataset. Besides, our model provides continuous low-dimensional representations of the data which can be a useful tool to visualize mixed datasets. Finally, we validate the performance of our approach by comparing its results with state-of-the-art mixed data clustering models over several commonly used datasets.

Keywords: Binary and count data; Deep Gaussian Mixture Model; Generalized Linear Latent Variable Model; MCEM algorithm; Ordinal and categorical data; Two-heads architecture; 62H30 (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s11634-021-00466-3

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