Fuzzy K-means clustering models for triangular fuzzy time trajectories

Renato Coppi () and Pierpaolo D'Urso ()
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Renato Coppi: Università degli Studi di Roma “La Sapienza”
Pierpaolo D'Urso: Università degli Studi di Roma “La Sapienza”

Statistical Methods & Applications, 2002, vol. 11, issue 1, No 2, 40 pages

Abstract: Abstract We focus our attention on the classification of fuzzy time trajectories with triangular membership function, described by a given set of individuals. To this purpose, we adopt a fullyinformational approach, explicitly recognizing the informational nature shared by the ingredients of the classification procedure: the observed data (Empirical Information) and the classification model (Theoretical Information). In particular, by supposing that the informational paradigm has a fuzzy nature, we suggest three fuzzy clustering models allowing the classification of the triangular fuzzy time trajectories, based on the analysis of the cross sectional and/or longitudinal characteristics of their components (centers and spreads). Two applicative examples are illustrated.

Keywords: Informational support; Fuzzy time array; Fuzzy time trajectory; Triangular membership function; Cross sectional and/or longitudinal double fuzzy clustering (search for similar items in EconPapers)
Date: 2002
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/BF02511444

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