CEVCLUS: evidential clustering with instance-level constraints for relational data

Recent advances in clustering consider incorporating background knowledge in the partitioning algorithm, using, e.g., pairwise constraints between objects. As a matter of fact, prior information, when available, often makes it possible to better retrieve meaningful clusters in data. Here, this approach is investigated in the framework of belief functions, which allows us to handle the imprecision and the uncertainty of the clustering process. In this context, the EVCLUS algorithm was proposed for partitioning objects described by a dissimilarity matrix. It is extended here so as to take pairwise constraints into account, by adding a term to its objective function. This term corresponds to a penalty term that expresses pairwise constraints in the belief function framework. Various synthetic and real datasets are considered to demonstrate the interest of the proposed method, called CEVCLUS, and two applications are presented. The performances of CEVCLUS are also compared to those of other constrained clustering algorithms. V. Antoine⋆ Université Blaise Pascal, PRES Clermont Université LIMOS, UMR CNRS 6158 BP 10125, F-630000 Clermont-Ferrand, France E-mail: [email protected] Tel.: +33-473-40-52-13 B. Quost · T. Denoeux Université de Technologie de Compiègne Laboratoire Heudiasyc, UMR CNRS 7253 Compiègne, France M.-H. Masson Université de Picardie Jules Verne, IUT de l’Oise Laboratoire Heudiasyc, UMR CNRS 7253 Compiègne, France ⋆This work has been mostly developed while the author was with Heudiasyc.