We propose two robust fuzzy clustering techniques in the context of preference rankings to group judges into homogeneous clusters even in the case of contamination due to outliers or, more generally, noisy data. The two fuzzy C-Medoids clustering methods, based on the same suitable exponential transformation of the Kemeny distance, belong to two different approaches and differ in the way they introduce the fuzziness in the membership matrix, the one based on the “m” exponent and the other on the Shannon entropy. As far as the Kemeny distance is concerned, it is equivalent to the Kendall distance in the case of complete rankings but differs from the latter in the way of handling tied rankings. Simulations prove that our methods are able to recover the natural structure of the groups neutralizing the effect of possible noises and outliers. Two applications to real datasets are also provided.

A Kemeny Distance-Based Robust Fuzzy Clustering for Preference Data / D’Urso, Pierpaolo; Vitale, Vincenzina. - In: JOURNAL OF CLASSIFICATION. - ISSN 0176-4268. - (2022). [10.1007/s00357-022-09420-0]

A Kemeny Distance-Based Robust Fuzzy Clustering for Preference Data

D’Urso, Pierpaolo
;
Vitale, Vincenzina
2022

Abstract

We propose two robust fuzzy clustering techniques in the context of preference rankings to group judges into homogeneous clusters even in the case of contamination due to outliers or, more generally, noisy data. The two fuzzy C-Medoids clustering methods, based on the same suitable exponential transformation of the Kemeny distance, belong to two different approaches and differ in the way they introduce the fuzziness in the membership matrix, the one based on the “m” exponent and the other on the Shannon entropy. As far as the Kemeny distance is concerned, it is equivalent to the Kendall distance in the case of complete rankings but differs from the latter in the way of handling tied rankings. Simulations prove that our methods are able to recover the natural structure of the groups neutralizing the effect of possible noises and outliers. Two applications to real datasets are also provided.
2022
Preference data ; Kemeny distance ;Fuzzy clustering ; Robust method
01 Pubblicazione su rivista::01a Articolo in rivista
A Kemeny Distance-Based Robust Fuzzy Clustering for Preference Data / D’Urso, Pierpaolo; Vitale, Vincenzina. - In: JOURNAL OF CLASSIFICATION. - ISSN 0176-4268. - (2022). [10.1007/s00357-022-09420-0]
File allegati a questo prodotto
File Dimensione Formato  
template_rev4.pdf

solo gestori archivio

Tipologia: Documento in Post-print (versione successiva alla peer review e accettata per la pubblicazione)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 2.87 MB
Formato Adobe PDF
2.87 MB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1661184
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? 0
social impact