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A robust method for clustering football players with mixed attributes

Author

Listed:
  • Pierpaolo D’Urso

    (Sapienza - University of Rome)

  • Livia Giovanni

    (Luiss University - Viale Romania)

  • Vincenzina Vitale

    (Sapienza - University of Rome)

Abstract

A robust fuzzy clustering model for mixed data is proposed. For each variable, or attribute, a proper dissimilarity measure is computed and the clustering procedure combines the dissimilarity matrices with weights objectively computed during the optimization process. The weights reflect the relevance of each attribute type in the clustering results. A simulation study and an empirical application to football players data are presented that show the effectiveness of the proposed clustering algorithm in finding clusters that would be hidden unless a multi-attributes approach were used.

Suggested Citation

  • Pierpaolo D’Urso & Livia Giovanni & Vincenzina Vitale, 2023. "A robust method for clustering football players with mixed attributes," Annals of Operations Research, Springer, vol. 325(1), pages 9-36, June.
    • Handle: RePEc:spr:annopr:v:325:y:2023:i:1:d:10.1007_s10479-022-04558-x
    DOI: 10.1007/s10479-022-04558-x
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    References listed on IDEAS

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