A fuzzy relational clustering algorithm based on a dissimilarity measure extracted from data

@article{Corsini2004AFR,
  title={A fuzzy relational clustering algorithm based on a dissimilarity measure extracted from data},
  author={Paolo Corsini and Beatrice Lazzerini and Francesco Marcelloni},
  journal={IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)},
  year={2004},
  volume={34},
  pages={775-781}
}
One of the critical aspects of clustering algorithms is the correct identification of the dissimilarity measure used to drive the partitioning of the data set. The dissimilarity measure induces the cluster shape and therefore determines the success of clustering algorithms. As cluster shapes change from a data set to another, dissimilarity measures should be extracted from data. To this aim, we exploit some pairs of points with known dissimilarity value to teach a dissimilarity relation to a… CONTINUE READING
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