Mixed data Deep Gaussian Mixture Model: A clustering model for mixed datasets

• URL: http://arxiv.org/abs/2010.06661v2
• Date: Wed, 10 Mar 2021 18:20:58 GMT
• Title: Mixed data Deep Gaussian Mixture Model: A clustering model for mixed datasets
• Authors: Robin Fuchs, Denys Pommeret, Cinzia Viroli
• Abstract summary: We introduce a model-based clustering method called Mixed Deep Gaussian Mixture Model (MDGMM) This architecture is flexible and can be adapted to mixed as well as to continuous or non-continuous data. Our model provides continuous low-dimensional representations of the data which can be a useful tool to visualize mixed datasets.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 (MDGMM) 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 "on the fly". 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.

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