Clustering belief functions based on attracting and conflicting metalevel evidence
<|reference_start|>Clustering belief functions based on attracting and conflicting metalevel evidence: In this paper we develop a method for clustering belief functions based on attracting and conflicting metalevel evidence. Such clustering is done when the belief functions concern multiple events, and all belief functions are mixed up. The clustering process is used as the means for separating the belief functions into subsets that should be handled independently. While the conflicting metalevel evidence is generated internally from pairwise conflicts of all belief functions, the attracting metalevel evidence is assumed given by some external source.<|reference_end|>
@article{schubert2003clustering,
    title={Clustering belief functions based on attracting and conflicting
  metalevel evidence},
    author={Johan Schubert},
    journal={in Proceedings of the Ninth International Conference on
  Information Processing and Management of Uncertainty in Knowledge-based
  Systems (IPMU'02), pp. 571-578, Annecy, France, 1-5 July 2002},
    year={2003},
    number={FOI-S-0524-SE},
    archivePrefix={arXiv},
    eprint={cs/0305031},
    primaryClass={cs.AI cs.NE}
}