A robust method for clustering football players with mixed attributes

Pierpaolo D’Urso (), Livia Giovanni () and Vincenzina Vitale ()
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Pierpaolo D’Urso: Sapienza - University of Rome
Livia Giovanni: Luiss University - Viale Romania
Vincenzina Vitale: Sapienza - University of Rome

Annals of Operations Research, 2023, vol. 325, issue 1, No 2, 9-36

Abstract: 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.

Keywords: Mixed data; Fuzzy C-medoids clustering; Attribute weighting system; Noise cluster; Football players; Performance variables; Position variables (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10479-022-04558-x

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