Co-clustering documents and words using bipartite spectral graph partitioning
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Incorporating User Provided Constraints into Document Clustering
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
On context-aware co-clustering with metadata support
Journal of Intelligent Information Systems
Constrained co-clustering with non-negative matrix factorisation
International Journal of Business Intelligence and Data Mining
Combining co-clustering with noise detection for theme-based summarization
ACM Transactions on Speech and Language Processing (TSLP)
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In order to derive high quality information from text, the field of text mining has advanced swiftly from simple document clustering to co-clustering documents and words. However, document co-clustering without any prior knowledge or background information is a challenging problem. In this paper, we propose a Semi-Supervised Non-negative Matrix Factorization (SS-NMF) based framework for document co-clustering. Our method computes a new word-document matrix by incorporating user provided constraints through distance metric learning. Using an iterative algorithm, we perform tri-factorization of the new matrix to infer the document and word clusters. Through extensive experiments conducted on publicly available data sets, we demonstrate the superior performance of SS-NMF for document co-clustering.