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Exponential distance-based fuzzy clustering for interval-valued data

Author

Listed:
  • Pierpaolo D’Urso

    (Sapienza University of Rome)

  • Riccardo Massari

    (Sapienza University of Rome)

  • Livia De Giovanni

    (LUISS Guido Carli)

  • Carmela Cappelli

    (Università Federico II di Napoli)

Abstract

In several real life and research situations data are collected in the form of intervals, the so called interval-valued data. In this paper a fuzzy clustering method to analyse interval-valued data is presented. In particular, we address the problem of interval-valued data corrupted by outliers and noise. In order to cope with the presence of outliers we propose to employ a robust metric based on the exponential distance in the framework of the Fuzzy C-medoids clustering mode, the Fuzzy C-medoids clustering model for interval-valued data with exponential distance. The exponential distance assigns small weights to outliers and larger weights to those points that are more compact in the data set, thus neutralizing the effect of the presence of anomalous interval-valued data. Simulation results pertaining to the behaviour of the proposed approach as well as two empirical applications are provided in order to illustrate the practical usefulness of the proposed method.

Suggested Citation

  • Pierpaolo D’Urso & Riccardo Massari & Livia De Giovanni & Carmela Cappelli, 2017. "Exponential distance-based fuzzy clustering for interval-valued data," Fuzzy Optimization and Decision Making, Springer, vol. 16(1), pages 51-70, March.
    • Handle: RePEc:spr:fuzodm:v:16:y:2017:i:1:d:10.1007_s10700-016-9238-8
    DOI: 10.1007/s10700-016-9238-8
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    References listed on IDEAS

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    1. A. Silva & Paula Brito, 2015. "Discriminant Analysis of Interval Data: An Assessment of Parametric and Distance-Based Approaches," Journal of Classification, Springer;The Classification Society, vol. 32(3), pages 516-541, October.
    2. Pierpaolo D'Urso & Paolo Giordani, 2006. "A robust fuzzy k-means clustering model for interval valued data," Computational Statistics, Springer, vol. 21(2), pages 251-269, June.
    3. Luis Angel Garcia-Escudero & Alfonso Gordaliza, 2005. "A Proposal for Robust Curve Clustering," Journal of Classification, Springer;The Classification Society, vol. 22(2), pages 185-201, September.
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    Cited by:

    1. Shivam Gupta & Sachin Modgil & Samadrita Bhattacharyya & Indranil Bose, 2022. "Artificial intelligence for decision support systems in the field of operations research: review and future scope of research," Annals of Operations Research, Springer, vol. 308(1), pages 215-274, January.

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