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Clustering large mixed-type data with ordinal variables

Author

Listed:
  • Gero Szepannek

    (Stralsund University of Applied Sciences)

  • Rabea Aschenbruck

    (Stralsund University of Applied Sciences)

  • Adalbert Wilhelm

    (Constructor University Bremen)

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.

Suggested Citation

  • Gero Szepannek & Rabea Aschenbruck & Adalbert Wilhelm, 2025. "Clustering large mixed-type data with ordinal variables," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 19(3), pages 749-767, September.
    • Handle: RePEc:spr:advdac:v:19:y:2025:i:3:d:10.1007_s11634-024-00595-5
    DOI: 10.1007/s11634-024-00595-5
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