  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.
