Abstract
Semi-supervised clustering is often viewed as using labeled data to aid the clustering process. However, existing algorithms fail to consider dual constraints between data points (e.g. documents) and features (e.g. words). To address this problem, in this paper, we propose a novel semi-supervised document co-clustering model OSS-NMF via orthogonal nonnegative matrix tri-factorization. Our model incorporates prior knowledge both on document and word side to aid the new word-category and document-cluster matrices construction. Besides, we prove the correctness and convergence of our model to demonstrate its mathematical rigorous. Our experimental evaluations show that the proposed document clustering model presents remarkable performance improvements with certain constraints.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Xu, W., Liu, X., Gong, Y.: Document Clustering Based on Non-negative Matrix Factorization. In: Proceedings of the 26th ACM SIGIR Conference on Research and Development in Information Retrieval, Toronto, Canada, pp. 267–273 (2003)
Dhillon, I.S.: Co-clustering Documents and Words Using Bipartite Spectral Graph Partitioning. In: Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, California, USA, pp. 269–274 (2001)
Lee, D., Seung, H.S.: Algorithms for Non-negative Matrix Factorization. In: Proceedings of 15th Annual Conference on Neural Information Processing Systems, Vancouver, British Columbia, Canada, vol. 13, pp. 556–562 (2001)
Ding, C., Li, T., Peng, W., Park, H.: Orthogonal Nonnegative Matrix Tri-factorizations for Clustering. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Philadelphia, PA, USA, pp. 126–135 (2006)
Long, B., Zhang, Z., Wu, X., Yu, P.S.: Spectral Clustering for Multi-type Relational Data. In: Proceedings of 23rd International Conference on Machine Learning, Pittsburgh, Pennsylvania, USA, pp. 585–592 (2006)
Chen, Y.H., Wang, L.J., Dong, M.: Semi-supervised Document Clustering with Simultaneous Text Representation and Categorization. In: Buntine, W., Grobelnik, M., Mladenić, D., Shawe-Taylor, J. (eds.) ECML PKDD 2009. LNCS (LNAI), vol. 5781, pp. 211–226. Springer, Heidelberg (2009)
Wang, F., Li, T., Zhang, C.S.: Semi-Supervised Clustering via Matrix Factorization. In: Proceedings of The 8th SIAM Conference on Data Mining, Atlanta, Geogia, pp. 1–12 (2008)
Li, T., Ding, C., Zhang, Y., Shao, B.: Knowledge Transformation from Word Space to Document Space. In: Proceedings of the 31st Annual International ACM SIGIR conference on research and development in information retrieval, Singapore, pp. 187–194 (2008)
Li, T., Zhang, Y., Sindhwani, W.: A Non-negative Matrix Tri-factorization Approach to Sentiment Classification with Lexical Prior Knowledge. In: Proceedings of the 47th Annual Meeting of the ACL and the 4th IJCNLP of the AFNLP, Suntec, Singapore, pp. 244–252 (2009)
Ding, C.H., Li, T., Jordan, M.I.: Convex and Semi-nonnegative Matrix Factorizations. IEEE Transactions on Pattern Analysis and Machine Intelligence 99(1), 195–197 (2008)
Wagstaff, K., Cardie, C., Rogers, S., Schroedl, S.: Constrained K-means Clustering with Background Knowledge. In: Proceedings of the 18th International Conference on Machine Learning, Williamstown, MA, USA, pp. 577–584 (2001)
Dhillon, I., Mallela, S., Modha, D.S.: Information-Theoretic Co-clustering. In: Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, pp. 89–98 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ma, H., Zhao, W., Tan, Q., Shi, Z. (2010). Orthogonal Nonnegative Matrix Tri-factorization for Semi-supervised Document Co-clustering. In: Zaki, M.J., Yu, J.X., Ravindran, B., Pudi, V. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2010. Lecture Notes in Computer Science(), vol 6119. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13672-6_19
Download citation
DOI: https://doi.org/10.1007/978-3-642-13672-6_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13671-9
Online ISBN: 978-3-642-13672-6
eBook Packages: Computer ScienceComputer Science (R0)