Fuzzy K-medoids clustering models for fuzzy multivariate time trajectories

Renato Coppi (), Pierpaolo D’Urso () and Paolo Giordani ()
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Renato Coppi: Università di Roma “La Sapienza”, Dipartimento di Statistica, Probabilità e Statistiche Applicate
Pierpaolo D’Urso: Università del Molise, Dipartimento di Scienze Economiche, Gestionali e Sociali
Paolo Giordani: Università di Roma “La Sapienza”, Dipartimento di Statistica, Probabilità e Statistiche Applicate

A chapter in Compstat 2006 - Proceedings in Computational Statistics, 2006, pp 17-29 from Springer

Abstract: Abstract Following the fuzzy approach, the clustering problem concerning a set of fuzzy multivariate time trajectories is addressed. The obtained clusters are characterized by observed typical LR fuzzy time trajectories, medoids, belonging to the data set at hand. Two different clustering models are proposed according to the cross-sectional or longitudinal aspects of the time trajectories. An application to air pollution data is carried out.

Keywords: Fuzzy Approach; Fuzzy data time arrays; K-medoids clustering (search for similar items in EconPapers)
Date: 2006
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-7908-1709-6_2

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DOI: 10.1007/978-3-7908-1709-6_2

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