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In this work, a new prototype-based clustering method named EvidentialC-Medoids (ECMdd), which belongs to the family of medoid-based clustering forproximity data, is proposed as an extension of Fuzzy C-Medoids (FCMdd) on thetheoretical framework of belief functions. In the application of FCMdd andoriginal ECMdd, a single medoid (prototype), which is supposed to belong to theobject set, is utilized to represent one class. For the sake of clarity, thiskind of ECMdd using a single medoid is denoted by sECMdd. In real clusteringapplications, using only one pattern to capture or interpret a class may notadequately model different types of group structure and hence limits theclustering performance. In order to address this problem, a variation of ECMddusing multiple weighted medoids, denoted by wECMdd, is presented. UnlikesECMdd, in wECMdd objects in each cluster carry various weights describingtheir degree of representativeness for that class. This mechanism enables eachclass to be represented by more than one object. Experimental results insynthetic and real data sets clearly demonstrate the superiority of sECMdd andwECMdd. Moreover, the clustering results by wECMdd can provide richerinformation for the inner structure of the detected classes with the help ofprototype weights.

ECMdd: Evidential c-medoids clustering with multiple prototypes