Clustering decomposed belief functions using generalized weights of conflict

Authors:
Johan Schubert
Affiliations:
Department of Decision Support Systems, Division of Command and Control Systems, Swedish Defence Research Agency, SE-164 90 Stockholm, Sweden
Venue:
International Journal of Approximate Reasoning
Year:
2008

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Abstract

We develop a method for clustering all types of belief functions, in particular non-consonant belief functions. Such clustering is done when the belief functions concern multiple events, and all belief functions are mixed up. Clustering is performed by decomposing all belief functions into simple support and inverse simple support functions that are clustered based on their pairwise generalized weights of conflict, constrained by weights of attraction assigned to keep track of all decompositions. The generalized conflict c@?(-~,~) and generalized weight of conflict J^-@?(-~,~) are derived in the combination of simple support and inverse simple support functions.