Clustering large mixed-type data with ordinal variables

Gero Szepannek (), Rabea Aschenbruck and Adalbert Wilhelm
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Gero Szepannek: Stralsund University of Applied Sciences
Rabea Aschenbruck: Stralsund University of Applied Sciences
Adalbert Wilhelm: Constructor University Bremen

Advances in Data Analysis and Classification, 2025, vol. 19, issue 3, No 8, 749-767

Abstract: Abstract One of the most frequently used algorithms for clustering data with both numeric and categorical variables is the k-prototypes algorithm, an extension of the well-known k-means clustering. Gower’s distance denotes another popular approach for dealing with mixed-type data and is suitable not only for numeric and categorical but also for ordinal variables. In the paper a modification of the k-prototypes algorithm to Gower’s distance is proposed that ensures convergence. This provides a tool that allows to take into account ordinal information for clustering and can also be used for large data. A simulation study demonstrates convergence, good clustering results as well as small runtimes.

Keywords: Cluster analysis; Mixed-type data; Ordinal data; K-prototypes; Gower’s distance; 62H30 (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11634-024-00595-5

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