Clustering Approaches for Mixed‐Type Data: A Comparative Study

Badih Ghattas () and Alvaro Sanchez San-Benito
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Badih Ghattas: AMSE - Aix-Marseille Sciences Economiques - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique
Alvaro Sanchez San-Benito: Airbus Helicopters - Aeroport International de Marseille-Provence

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Abstract: 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.

Keywords: Bayesian networks; clustering; KAMILA; LCM; mixed-type data (search for similar items in EconPapers)
Date: 2025-01
Note: View the original document on HAL open archive server: https://hal.science/hal-05069567v1
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Published in Journal of Probability and Statistics, 2025, 2025 (1), ⟨10.1155/jpas/2242100⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05069567

DOI: 10.1155/jpas/2242100

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