Clustering mixed data presents numerous challenges inherent to the very heterogeneous nature of the variables. A clustering algorithm should be able, despite of 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 comparing its results with state-of-the-art mixed data clustering models over several commonly used datasets.

Mixed Deep Gaussian Mixture Model: a clustering model for mixed datasets / Fuchs, Robin; Pommeret, Denys; Viroli, Cinzia. - In: ADVANCES IN DATA ANALYSIS AND CLASSIFICATION. - ISSN 1862-5355. - ELETTRONICO. - 1:(2021), pp. 1-23. [10.1007/s11634-021-00466-3]

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

Viroli, Cinzia
2021

Abstract

Clustering mixed data presents numerous challenges inherent to the very heterogeneous nature of the variables. A clustering algorithm should be able, despite of 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 comparing its results with state-of-the-art mixed data clustering models over several commonly used datasets.
2021
Mixed Deep Gaussian Mixture Model: a clustering model for mixed datasets / Fuchs, Robin; Pommeret, Denys; Viroli, Cinzia. - In: ADVANCES IN DATA ANALYSIS AND CLASSIFICATION. - ISSN 1862-5355. - ELETTRONICO. - 1:(2021), pp. 1-23. [10.1007/s11634-021-00466-3]
Fuchs, Robin; Pommeret, Denys; Viroli, Cinzia
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/840189
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