Unsupervised learning of prototypes and attribute weights

@article{Frigui2004UnsupervisedLO,
  title={Unsupervised learning of prototypes and attribute weights},
  author={Hichem Frigui and Olfa Nasraoui},
  journal={Pattern Recognition},
  year={2004},
  volume={37},
  pages={567-581}
}
In this paper, we introduce new algorithms that perform clustering and feature weighting simultaneously and in an unsupervised manner. The proposed algorithms are computationally and implementationally simple, and learn a di1erent set of feature weights for each identi2ed cluster. The cluster dependent feature weights o1er two advantages. First, they guide the clustering process to partition the data set into more meaningful clusters. Second, they can be used in the subsequent steps of a… CONTINUE READING
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