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A Specialized Xie-Beni Measure for Clustering with Adaptive Distance

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Fuzzy Logic and Technology, and Aggregation Operators (EUSFLAT 2023, AGOP 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14069))

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Abstract

To certify good data partitioning, it is necessary to use an evaluation measure. This measure must take into account the specificity of the modeled partition. For centroid-based fuzzy partitioning, different measures exist. However, none of them takes into account the adaptive distance that some clustering models use. In our study, we extend the Xie-Beni measure, using both the Mahalanobis distance and the Wasserstein distance. The numerical results show the relevance of our new index.

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Notes

  1. 1.

    https://archive.ics.uci.edu/ml/datasets.php.

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Author information

Authors and Affiliations

  1. Technische Universität, Munich, Germany

    Shidi Deng

  2. LIMOS, Université Clermont Auvergne, Clermont-Ferrand, France

    Shidi Deng, Benoit Albert, Violaine Antoine & Jonas Koko

Authors
  1. Shidi Deng
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  2. Benoit Albert
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  3. Violaine Antoine
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  4. Jonas Koko
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Corresponding author

Correspondence to Benoit Albert .

Editor information

Editors and Affiliations

  1. University of the Balearic Islands, Palma, Spain

    Sebastia Massanet

  2. University of Oviedo, Gijón, Spain

    Susana Montes

  3. University of the Balearic Islands, Palma, Spain

    Daniel Ruiz-Aguilera

  4. University of the Balearic Islands, Palma, Spain

    Manuel González-Hidalgo

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Deng, S., Albert, B., Antoine, V., Koko, J. (2023). A Specialized Xie-Beni Measure for Clustering with Adaptive Distance. In: Massanet, S., Montes, S., Ruiz-Aguilera, D., González-Hidalgo, M. (eds) Fuzzy Logic and Technology, and Aggregation Operators. EUSFLAT AGOP 2023 2023. Lecture Notes in Computer Science, vol 14069. Springer, Cham. https://doi.org/10.1007/978-3-031-39965-7_59

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  • DOI: https://doi.org/10.1007/978-3-031-39965-7_59

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-031-39965-7

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