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A Fuzzy Clustering Model for Fuzzy Data with Outliers

Author

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  • M. H. Fazel Zarandi

    (Amirkabir University of Technology, Iran)

  • Zahra S. Razaee

    (Amirkabir University of Technology, Iran)

Abstract

This paper proposes a fuzzy clustering model for fuzzy data with outliers. The model is based on Wasserstein distance between interval valued data, which is generalized to fuzzy data. In addition, Keller’s approach is used to identify outliers and reduce their influences. The authors also define a transformation to change the distance to the Euclidean distance. With the help of this approach, the problem of fuzzy clustering of fuzzy data is reduced to fuzzy clustering of crisp data. In order to show the performance of the proposed clustering algorithm, two simulation experiments are discussed.

Suggested Citation

  • M. H. Fazel Zarandi & Zahra S. Razaee, 2011. "A Fuzzy Clustering Model for Fuzzy Data with Outliers," International Journal of Fuzzy System Applications (IJFSA), IGI Global, vol. 1(2), pages 29-42, April.
    • Handle: RePEc:igg:jfsa00:v:1:y:2011:i:2:p:29-42
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    Cited by:

    1. Ferraro, Maria Brigida, 2024. "Fuzzy k-Means: history and applications," Econometrics and Statistics, Elsevier, vol. 30(C), pages 110-123.

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