Article Dans Une Revue Journal of Probability and Statistics Année : 2025

Clustering Approaches for Mixed‐Type Data: A Comparative Study

Alvaro Sanchez San-Benito

Résumé

Clustering is widely used in unsupervised learning to fnd homogeneous groups of observations within a dataset. However, clustering mixed-type data remains a challenge, as few existing approaches are suited for this task. Tis study presents the state-of-the-art of these approaches and compares them using various simulation models. Te compared methods include the distance-based approaches k-prototypes, PDQ, and convex k-means, and the probabilistic methods KAy-means for MIxed LArge data (KAMILA), the mixture of Bayesian networks (MBNs), and latent class model (LCM). Te aim is to provide insights into the behavior of diferent methods across a wide range of scenarios by varying some experimental factors such as the number of clusters, cluster overlap, sample size, dimension, proportion of continuous variables in the dataset, and clusters' distribution. Te degree of cluster overlap and the proportion of continuous variables in the dataset and the sample size have a signifcant impact on the observed performances. When strong interactions exist between variables alongside an explicit dependence on cluster membership, none of the evaluated methods demonstrated satisfactory performance. In our experiments KAMILA, LCM, and k-prototypes exhibited the best performance, with respect to the adjusted rand index (ARI). All the methods are available in R.

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hal-05069567 , version 1 (15-05-2025)

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Badih Ghattas, Alvaro Sanchez San-Benito. Clustering Approaches for Mixed‐Type Data: A Comparative Study. Journal of Probability and Statistics, 2025, 2025 (1), ⟨10.1155/jpas/2242100⟩. ⟨hal-05069567⟩
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