Benchmarking distance-based partitioning methods for mixed-type data

TL;DR

This paper benchmarks eight distance-based clustering methods for mixed-type data, analyzing their performance across various data scenarios to guide practitioners in selecting the most effective approach.

Contribution

It provides a comprehensive comparison of clustering methods for mixed data, highlighting their relative performance and influencing factors.

Findings

KAMILA, K-Prototypes, and sequential Factor Analysis with K-Means perform best in many scenarios.

Cluster overlap and variable composition significantly affect clustering success.

The study offers practical insights for choosing appropriate clustering methods.

Abstract

Clustering mixed-type data, that is, observation by variable data that consist of both continuous and categorical variables poses novel challenges. Foremost among these challenges is the choice of the most appropriate clustering method for the data. This paper presents a benchmarking study comparing eight distance-based partitioning methods for mixed-type data in terms of cluster recovery performance. A series of simulations carried out by a full factorial design are presented that examined the effect of a variety of factors on cluster recovery. The amount of cluster overlap, the percentage of categorical variables in the data set, the number of clusters and the number of observations had the largest effects on cluster recovery and in most of the tested scenarios. KAMILA, K-Prototypes and sequential Factor Analysis and K-Means clustering typically performed better than other methods. The study can be a useful reference for practitioners in the choice of the most appropriate method.