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.

A robust method for clustering football players with mixed attributes / D'Urso, P.; De Giovanni, L.; Vitale, V.. - In: ANNALS OF OPERATIONS RESEARCH. - ISSN 0254-5330. - (2023). [10.1007/s10479-022-04558-x]

A robust method for clustering football players with mixed attributes

D'Urso P.;Vitale V.
2023

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.
2023
Attribute weighting system; Football players; Fuzzy C-medoids clustering; Mixed data; Noise cluster; Performance variables; Position variables
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A robust method for clustering football players with mixed attributes / D'Urso, P.; De Giovanni, L.; Vitale, V.. - In: ANNALS OF OPERATIONS RESEARCH. - ISSN 0254-5330. - (2023). [10.1007/s10479-022-04558-x]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1613526
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