In this paper we extend an earlier result within Dempster-Shafer theory["Fast Dempster-Shafer Clustering Using a Neural Network Structure," in Proc.Seventh Int. Conf. Information Processing and Management of Uncertainty inKnowledge-Based Systems (IPMU'98)] where several pieces of evidence wereclustered into a fixed number of clusters using a neural structure. This wasdone by minimizing a metaconflict function. We now develop a method forsimultaneous clustering and determination of number of clusters duringiteration in the neural structure. We let the output signals of neuronsrepresent the degree to which a pieces of evidence belong to a correspondingcluster. From these we derive a probability distribution regarding the numberof clusters, which gradually during the iteration is transformed into adetermination of number of clusters. This gradual determination is fed backinto the neural structure at each iteration to influence the clusteringprocess.
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