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Automatic clustering algorithm for fuzzy data

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  • Wen-Liang Hung
  • Jenn-Hwai Yang

Abstract

Coppi et al. [7] applied Yang and Wu's [20] idea to propose a possibilistic k -means (P k M) clustering algorithm for LR -type fuzzy numbers. The memberships in the objective function of P k M no longer need to satisfy the constraint in fuzzy k -means that of a data point across classes sum to one. However, the clustering performance of P k M depends on the initializations and weighting exponent. In this paper, we propose a robust clustering method based on a self-updating procedure. The proposed algorithm not only solves the initialization problems but also obtains a good clustering result. Several numerical examples also demonstrate the effectiveness and accuracy of the proposed clustering method, especially the robustness to initial values and noise. Finally, three real fuzzy data sets are used to illustrate the superiority of this proposed algorithm.

Suggested Citation

  • Wen-Liang Hung & Jenn-Hwai Yang, 2015. "Automatic clustering algorithm for fuzzy data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(7), pages 1503-1518, July.
    • Handle: RePEc:taf:japsta:v:42:y:2015:i:7:p:1503-1518
    DOI: 10.1080/02664763.2014.1001326
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    Cited by:

    1. Tai VoVan & Thao Nguyen Trang, 2018. "Similar Coefficient of Cluster for Discrete Elements," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 80(1), pages 19-36, May.
    2. Thao Nguyentrang & Tai Vovan, 2017. "Fuzzy clustering of probability density functions," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(4), pages 583-601, March.

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