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Fuzzy K-means clustering models for triangular fuzzy time trajectories

Author

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  • Renato Coppi

    (Università degli Studi di Roma “La Sapienza”)

  • Pierpaolo D'Urso

    (Università degli Studi di Roma “La Sapienza”)

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.

Suggested Citation

  • Renato Coppi & Pierpaolo D'Urso, 2002. "Fuzzy K-means clustering models for triangular fuzzy time trajectories," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 11(1), pages 21-40, February.
    • Handle: RePEc:spr:stmapp:v:11:y:2002:i:1:d:10.1007_bf02511444
    DOI: 10.1007/BF02511444
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    References listed on IDEAS

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    1. Willem Heiser & Patrick Groenen, 1997. "Cluster differences scaling with a within-clusters loss component and a fuzzy successive approximation strategy to avoid local minima," Psychometrika, Springer;The Psychometric Society, vol. 62(1), pages 63-83, March.
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    Cited by:

    1. Pierpaolo D’Urso & Livia De Giovanni & Riccardo Massari & Francesca G. M. Sica, 2019. "Cross Sectional and Longitudinal Fuzzy Clustering of the NUTS and Positioning of the Italian Regions with Respect to the Regional Competitiveness Index (RCI) Indicators with Contiguity Constraints," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 146(3), pages 609-650, December.
    2. B. Lafuente-Rego & P. D’Urso & J. A. Vilar, 2020. "Robust fuzzy clustering based on quantile autocovariances," Statistical Papers, Springer, vol. 61(6), pages 2393-2448, December.
    3. D'Urso, Pierpaolo & Disegna, Marta & Massari, Riccardo & Osti, Linda, 2016. "Fuzzy segmentation of postmodern tourists," Tourism Management, Elsevier, vol. 55(C), pages 297-308.
    4. Coppi, Renato & D'Urso, Pierpaolo, 2003. "Three-way fuzzy clustering models for LR fuzzy time trajectories," Computational Statistics & Data Analysis, Elsevier, vol. 43(2), pages 149-177, June.

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