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Trimmed fuzzy clustering for interval-valued data

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Abstract

In this paper, following a partitioning around medoids approach, a fuzzy clustering model for interval-valued data, i.e., FCMd-ID, is introduced. Successively, for avoiding the disruptive effects of possible outlier interval-valued data in the clustering process, a robust fuzzy clustering model with a trimming rule, called Trimmed Fuzzy \(C\)-medoids for interval-valued data (TrFCMd-ID), is proposed. In order to show the good performances of the robust clustering model, a simulation study and two applications are provided.

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Acknowledgments

The authors thank the editors and the three referees for their useful comments and suggestions which helped to improve the quality and presentation of this manuscript.

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Authors and Affiliations

  1. Dipartimento di Scienze Sociali ed Economiche, Sapienza, Università di Roma, P.za Aldo Moro, 5, 00185 , Rome, Italy

    Pierpaolo D’Urso & Riccardo Massari

  2. Dipartimento di Scienze Politiche, LUISS Guido Carli, Viale Romania, 32, 00197 , Rome, Italy

    Livia De Giovanni

Authors
  1. Pierpaolo D’Urso
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  2. Livia De Giovanni
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  3. Riccardo Massari
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Correspondence to Pierpaolo D’Urso.

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D’Urso, P., De Giovanni, L. & Massari, R. Trimmed fuzzy clustering for interval-valued data. Adv Data Anal Classif 9, 21–40 (2015). https://doi.org/10.1007/s11634-014-0169-3

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