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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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    1. Cuadras, C. M. & Fortiana, J., 1995. "A Continuous Metric Scaling Solution for a Random Variable," Journal of Multivariate Analysis, Elsevier, vol. 52(1), pages 1-14, January.
    2. Boj, Eva & Grané, Aurea, 2024. "The robustification of distance-based linear models: Some proposals," Socio-Economic Planning Sciences, Elsevier, vol. 95(C).
    3. J. Gower & P. Legendre, 1986. "Metric and Euclidean properties of dissimilarity coefficients," Journal of Classification, Springer;The Classification Society, vol. 3(1), pages 5-48, March.
    4. Grané, Aurea & Salini, Silvia & Verdolini, Elena, 2021. "Robust multivariate analysis for mixed-type data: Novel algorithm and its practical application in socio-economic research," Socio-Economic Planning Sciences, Elsevier, vol. 73(C).
    5. Lawrence Hubert & Phipps Arabie, 1985. "Comparing partitions," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 193-218, December.
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