  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 in
Knowledge-Based Systems (IPMU 98)] where a large number of pieces of evidence
are clustered into subsets by a neural network structure. The clustering is
done by minimizing a metaconflict function. Previously we developed a method
based on iterative optimization. While the neural method had a much lower
computation time than iterative optimization its average clustering performance
was not as good. Here, we develop a hybrid of the two methods. We let the
neural structure do the initial clustering in order to achieve a high
computational performance. Its solution is fed as the initial state to the
iterative optimization in order to improve the clustering performance.
