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A robust fuzzy k-means clustering model for interval valued data

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Summary

In this paper a robust fuzzy k-means clustering model for interval valued data is introduced. The peculiarity of the proposed model is the capability to manage anomalous interval valued data by reducing the effects of such outliers in the clustering model. In the interval case, the concept of anomalous data involves both the center and the width (the radius) of an interval. In order to show how our model works the results of a simulation experiment and an application to real interval valued data are discussed.

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Authors and Affiliations

  1. Dipartimento di Scienze Economiche, Gestionali e Sociali, Università degli Studi del Molise, Via De Sanctis, 86100, Campobasso, Italy

    Pierpaolo D’Urso

  2. Dipartimento di Statistica Probabilità e Statistiche Applicate, Università di Roma “La Sapienza”, P.le Aldo Moro, 5, 00185, Rome, Italy

    Paolo Giordani

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  1. Pierpaolo D’Urso
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  2. Paolo Giordani
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D’Urso, P., Giordani, P. A robust fuzzy k-means clustering model for interval valued data. Computational Statistics 21, 251–269 (2006). https://doi.org/10.1007/s00180-006-0262-y

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