CECM

CECM: constrained evidential C-means algorithm 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.


References in zbMATH (referenced in 6 articles , 1 standard article )

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  1. Bahri, Nassim; Bach Tobji, Mohamed Anis: On indexing evidential data (2019)
  2. Antoine, V.; Quost, B.; Masson, M.-H.; Denoeux, T.: CEVCLUS: evidential clustering with instance-level constraints for relational data (2014) ioport
  3. Lelandais, Benoît; Gardin, Isabelle; Mouchard, Laurent; Vera, Pierre; Ruan, Su: Dealing with uncertainty and imprecision in image segmentation using belief function theory (2014) ioport
  4. Antoine, V.; Quost, B.; Masson, M.-H.; Denœux, T.: CECM: constrained evidential (C)-means algorithm (2012)
  5. Serir, Lisa; Ramasso, Emmanuel; Zerhouni, Noureddine: Evidential evolving Gustafson-Kessel algorithm for online data streams partitioning using belief function theory (2012) ioport
  6. Serir, Lisa; Ramasso, Emmanuel; Zerhouni, Noureddine: E2GK: evidential evolving Gustafsson-Kessel algorithm for data streams partitioning using belief functions (2011) ioport