A matrix-based approach for semi-supervised document co-clustering
Proceedings of the 17th ACM conference on Information and knowledge management
Semi-supervised Document Clustering with Simultaneous Text Representation and Categorization
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Supervised input space scaling for non-negative matrix factorization
Signal Processing
Constrained co-clustering with non-negative matrix factorisation
International Journal of Business Intelligence and Data Mining
On Knowledge-Enhanced Document Clustering
International Journal of Information Retrieval Research
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Document clustering without any prior knowledge or background information is a challenging problem. In this paper, we propose SS-NMF: a semi-supervised nonnegative 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 documentdocument 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.