Fast Dempster-Shafer clustering using a neural network structure

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dc.creatorSchubert, Johan
dc.date2003-05-16
dc.date.accessioned2026-07-07T03:19:41Z
dc.date.available2026-07-07T03:19:41Z
dc.descriptionIn 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.
dc.description8 pages, 7 figures
dc.identifierhttps://arxiv.org/abs/cs/0305023
dc.identifierhttp://arxiv.org/abs/cs/0305023
dc.identifierin Proceedings of the Seventh International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems (IPMU'98), pp. 1438-1445, Universite' de La Sorbonne, Paris, France, 6-10 July 1998, Editions EDK, Paris, 1998
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/31560
dc.subjectArtificial Intelligence
dc.subjectNeural and Evolutionary Computing
dc.subjectI.2.3; I.2.6; I.5.3
dc.titleFast Dempster-Shafer clustering using a neural network structure
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