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CECM: Constrained evidential C-means algorithm

Author

Listed:
  • Antoine, V.
  • Quost, B.
  • Masson, M.-H.
  • Denœux, T.

Abstract

In clustering applications, prior knowledge about cluster membership is sometimes available. To integrate such auxiliary information, constraint-based (or semi-supervised) methods have been proposed in the hard or fuzzy clustering frameworks. This approach is extended to evidential clustering, in which the membership of objects to clusters is described by belief functions. A variant of the Evidential C-means (ECM) algorithm taking into account pairwise constraints is proposed. These constraints are translated into the belief function framework and integrated in the cost function. Experiments with synthetic and real data sets demonstrate the interest of the method. In particular, an application to medical image segmentation is presented.

Suggested Citation

  • Antoine, V. & Quost, B. & Masson, M.-H. & Denœux, T., 2012. "CECM: Constrained evidential C-means algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 56(4), pages 894-914.
    • Handle: RePEc:eee:csdana:v:56:y:2012:i:4:p:894-914
    DOI: 10.1016/j.csda.2010.09.021
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    References listed on IDEAS

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    1. Gordon, A. D., 1996. "A survey of constrained classification," Computational Statistics & Data Analysis, Elsevier, vol. 21(1), pages 17-29, January.
    2. Berget, Ingunn & Mevik, Bjorn-Helge & Naes, Tormod, 2008. "New modifications and applications of fuzzy C-means methodology," Computational Statistics & Data Analysis, Elsevier, vol. 52(5), pages 2403-2418, January.
    3. Baudrit, C. & Dubois, D., 2006. "Practical representations of incomplete probabilistic knowledge," Computational Statistics & Data Analysis, Elsevier, vol. 51(1), pages 86-108, November.
    4. Doring, Christian & Lesot, Marie-Jeanne & Kruse, Rudolf, 2006. "Data analysis with fuzzy clustering methods," Computational Statistics & Data Analysis, Elsevier, vol. 51(1), pages 192-214, November.
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    Cited by:

    1. Montes, Ignacio & Miranda, Enrique & Montes, Susana, 2014. "Stochastic dominance with imprecise information," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 868-886.
    2. Zhi-gang Su & Hong-yu Zhou & Yong-sheng Hao, 2021. "Evidential evolving C-means clustering method based on artificial bee colony algorithm with variable strings and interactive evaluation mode," Fuzzy Optimization and Decision Making, Springer, vol. 20(3), pages 293-313, September.

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