Simultaneous Dempster-Shafer clustering and gradual determination of number of clusters using a neural network structure
<|reference_start|>Simultaneous Dempster-Shafer clustering and gradual determination of number of clusters using a neural network structure: 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 several pieces of evidence were clustered into a fixed number of clusters using a neural structure. This was done by minimizing a metaconflict function. We now develop a method for simultaneous clustering and determination of number of clusters during iteration in the neural structure. We let the output signals of neurons represent the degree to which a pieces of evidence belong to a corresponding cluster. From these we derive a probability distribution regarding the number of clusters, which gradually during the iteration is transformed into a determination of number of clusters. This gradual determination is fed back into the neural structure at each iteration to influence the clustering process.<|reference_end|>
@article{schubert2003simultaneous,
    title={Simultaneous Dempster-Shafer clustering and gradual determination of
  number of clusters using a neural network structure},
    author={Johan Schubert},
    journal={in Proceedings of the 1999 Information, Decision and Control
  Conference (IDC'99), pp. 401-406, Adelaide, Australia, 8-10 February 1999,
  IEEE, Piscataway, 1999},
    year={2003},
    doi={10.1109/IDC.1999.754191},
    number={FOA-B-99-00431-505-SE},
    archivePrefix={arXiv},
    eprint={cs/0305025},
    primaryClass={cs.AI cs.NE}
}