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Fast k-medoids and q-Fold Fast k-medoids: New distance-based clustering algorithms for large mixed-type data

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  • Grané Chávez, Aurea
  • Scielzo Ortiz, Fabio

Abstract

In this work new robust efficient clustering algorithms for large datasets of mixedtype data are proposed and implemented in a new Python package called FastKmedoids. Their performance is analyzed through an extensive simulation study, and compared to a wide range of existing clustering alternatives in terms of both predictive power and computational efficiency. MDS is used to visualize clustering results.

Suggested Citation

  • Grané Chávez, Aurea & Scielzo Ortiz, Fabio, 2025. "Fast k-medoids and q-Fold Fast k-medoids: New distance-based clustering algorithms for large mixed-type data," DES - Working Papers. Statistics and Econometrics. WS 46673, Universidad Carlos III de Madrid. Departamento de Estadística.
    • Handle: RePEc:cte:wsrepe:46673
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    Keywords

    Clustering;

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