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1 School of Automation Northwestern Polytechnical University 2 DRUID - Declarative & Reliable management of Uncertain, user-generated Interlinked Data IRISA-D7 - GESTION DES DONNÉES ET DE LA CONNAISSANCE

Abstract : In real clustering applications, proximity data, in which only pairwise similarities or dissimilarities are known, is more general than object data, in which each pattern is described explicitly by a list of attributes. Medoid-based clustering algorithms, which assume the prototypes of classes are objects, are of great value for partitioning relational data sets. In this paper a new prototype-based clustering method, named Evidential C-Medoids ECMdd, which is an extension of Fuzzy C-Medoids FCMdd on the theoretical framework of belief functions is proposed. In ECMdd, medoids are utilized as the prototypes to represent the detected classes, including specific classes and imprecise classes. Specific classes are for the data which are distinctly far from the prototypes of other classes, while imprecise classes accept the objects that may be close to the prototypes of more than one class. This soft decision mechanism could make the clustering results more cautious and reduce the misclassification rates. Experiments in synthetic and real data sets are used to illustrate the performance of ECMdd. The results show that ECMdd could capture well the uncertainty in the internal data structure. Moreover, it is more robust to the initializations compared with FCMdd.

Keywords : Relational clustering Credal partitions Eviden-tial c-medoids Imprecise classes

Author: Kuang Zhou - Arnaud Martin - Quan Pan - Zhun-Ga Liu -

Source: https://hal.archives-ouvertes.fr/


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