  In this paper we study a problem within Dempster-Shafer theory where 2**n - 1
pieces of evidence are clustered by a neural structure into n clusters. The
clustering is done by minimizing a metaconflict function. Previously we
developed a method based on iterative optimization. However, for large scale
problems we need a method with lower computational complexity. The neural
structure was found to be effective and much faster than iterative optimization
for larger problems. While the growth in metaconflict was faster for the neural
structure compared with iterative optimization in medium sized problems, the
metaconflict per cluster and evidence was moderate. The neural structure was
able to find a global minimum over ten runs for problem sizes up to six
clusters.
