Semi-Supervised Fuzzy Clustering with Pairwise-Constrained Competitive Agglomeration

  title={Semi-Supervised Fuzzy Clustering with Pairwise-Constrained Competitive Agglomeration},
  author={Nizar Grira and Michel Crucianu and Nozha Boujemaa},
  journal={The 14th IEEE International Conference on Fuzzy Systems, 2005. FUZZ '05.},
Traditional clustering algorithms usually rely on a pre-defined similarity measure between unlabelled data to attempt to identify natural classes of items. When compared to what a human expert would provide on the same data, the results obtained may be disappointing if the similarity measure employed by the system is too different from the one a human would use. To obtain clusters fitting user expectations better, we can exploit, in addition to the unlabelled data, some limited form of… CONTINUE READING
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