Clustering Approaches for Mixed-Type Data: A Comparative Study

TL;DR

This paper compares various clustering methods for mixed-type data, analyzing their performance across different scenarios to identify the most effective approaches and understand their limitations.

Contribution

It provides a comprehensive comparison of state-of-the-art clustering methods for mixed-type data, highlighting their strengths and weaknesses in diverse conditions.

Findings

KAMILA, LCM, and k-prototypes performed best in ARI.

Performance is significantly affected by cluster overlap, variable proportion, and sample size.

None of the methods excel when variables strongly interact with cluster membership.

Abstract

Clustering is widely used in unsupervised learning to find homogeneous groups of observations within a dataset. However, clustering mixed-type data remains a challenge, as few existing approaches are suited for this task. This study presents the state-of-the-art of these approaches and compares them using various simulation models. The 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). The aim is to provide insights into the behavior of different 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. The degree of cluster overlap and the proportion of continuous variables in the dataset and the sample size have a significant 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.