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- W2124387682 abstract "Document clustering without any prior knowledge or background information is a challenging problem. In this paper, we propose SS-NMF: a semi-supervised non- negative matrix factorization framework for document clustering. In SS-NMF, users are able to provide supervision for document clustering in terms of pairwise constraints on a few documents specifying whether they must or cannot be clustered together. Through an iterative algorithm, we perform symmetric tri-factorization of the document- document similarity matrix to infer the document clusters. Theoretically, we show that SS-NMF provides a general framework for semi-supervised clustering and that existing approaches can be considered as special cases of SS-NMF. Through extensive experiments conducted on publicly available data sets, we demonstrate the superior performance of SS-NMF for clustering documents." @default.
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- W2124387682 date "2007-10-01" @default.
- W2124387682 modified "2023-09-26" @default.
- W2124387682 title "Incorporating User Provided Constraints into Document Clustering" @default.
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- W2124387682 doi "https://doi.org/10.1109/icdm.2007.67" @default.
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