PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

CECM: Constrained Evidential C-Means algorithm
Violaine Antoine, B. Quost, M.-H. Masson and T. Denoeux
Computational Statistics and Data Analysis 2010.


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.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
ID Code:7769
Deposited By:Violaine Antoine
Deposited On:17 March 2011