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
Parameter-less co-clustering for star-structured heterogeneous data
Data Mining and Knowledge Discovery
Fuzzy semi-supervised co-clustering for text documents
Fuzzy Sets and Systems
Semi-supervised clustering via constrained symmetric non-negative matrix factorization
BI'12 Proceedings of the 2012 international conference on Brain Informatics
Subtractive clustering for seeding non-negative matrix factorizations
Information Sciences: an International Journal
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Coclustering heterogeneous data has attracted extensive attention recently due to its high impact on various important applications, such us text mining, image retrieval, and bioinformatics. However, data coclustering without any prior knowledge or background information is still a challenging problem. In this paper, we propose a Semisupervised Non-negative Matrix Factorization (SS-NMF) framework for data coclustering. Specifically, our method computes new relational matrices by incorporating user provided constraints through simultaneous distance metric learning and modality selection. Using an iterative algorithm, we then perform trifactorizations of the new matrices to infer the clusters of different data types and their correspondence. Theoretically, we prove the convergence and correctness of SS-NMF coclustering and show the relationship between SS-NMF with other well-known coclustering models. Through extensive experiments conducted on publicly available text, gene expression, and image data sets, we demonstrate the superior performance of SS-NMF for heterogeneous data coclustering.