ECMdd: Evidential c-medoids clustering with multiple prototypes
  In this work, a new prototype-based clustering method named Evidential
C-Medoids (ECMdd), which belongs to the family of medoid-based clustering for
proximity data, is proposed as an extension of Fuzzy C-Medoids (FCMdd) on the
theoretical framework of belief functions. In the application of FCMdd and
original ECMdd, a single medoid (prototype), which is supposed to belong to the
object set, is utilized to represent one class. For the sake of clarity, this
kind of ECMdd using a single medoid is denoted by sECMdd. In real clustering
applications, using only one pattern to capture or interpret a class may not
adequately model different types of group structure and hence limits the
clustering performance. In order to address this problem, a variation of ECMdd
using multiple weighted medoids, denoted by wECMdd, is presented. Unlike
sECMdd, in wECMdd objects in each cluster carry various weights describing
their degree of representativeness for that class. This mechanism enables each
class to be represented by more than one object. Experimental results in
synthetic and real data sets clearly demonstrate the superiority of sECMdd and
wECMdd. Moreover, the clustering results by wECMdd can provide richer
information for the inner structure of the detected classes with the help of
prototype weights.
