Non-negative matrix factorization for semi-supervised data clustering

@article{Chen2008NonnegativeMF,
  title={Non-negative matrix factorization for semi-supervised data clustering},
  author={Yanhua Chen and Manjeet Rege and Ming Dong and Jing Hua},
  journal={Knowledge and Information Systems},
  year={2008},
  volume={17},
  pages={355-379}
}
Traditional clustering algorithms are inapplicable to many real-world problems where limited knowledge from domain experts is available. Incorporating the domain knowledge can guide a clustering algorithm, consequently improving the quality of clustering. In this paper, we propose SS-NMF: a semi-supervised non-negative matrix factorization framework for data clustering. In SS-NMF, users are able to provide supervision for clustering in terms of pairwise constraints on a few data objects… CONTINUE READING

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