Abstract
This paper investigates the idea of incorporating incremental user feedbacks and a small amount of sample documents for some, not necessarily all, clusters into text clustering. For the modeling of each cluster, we make use of a local weight metric to reflect the importance of the features for a particular cluster. The local weight metric is learned using both the unlabeled data and the constraints generated automatically from user feedbacks and sample documents. The quality of local metric is improved by incorporating more precise constraints. Improving the quality of local metric will in return enhance the clustering performance. We have conducted extensive experiments on real-world news documents. The results demonstrate that user feedback information coupled with local metric learning can dramatically improve the clustering performance.
This paper is substantially supported by grants from the Research Grant Council of the Hong Kong Special Administrative Region, China (Project Nos: CUHK 4179/03E and CUHK4193/04E), the Direct Grant of the Faculty of Engineering, CUHK (Project Code: 2050363), and CUHK Strategic Grant (No: 4410001). This work is also affiliated with the Microsoft-CUHK Joint Laboratory for Human-centric Computing and Interface Technologies.
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References
Basu, S., Banerjee, A., Mooney, R.: Semi-supervised clustering by seeding. In: Proceedings of the Nineteenth International conference on Machine Learning (2002)
Basu, S., Bilenko, M., Mooney, R.J.: A probabilistic framework for semisupervised clustering. In: Proceedings of the tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 59–68 (2004)
Bilenko, M., Basu, S., Mooney, R.J.: Integrating constraints and metric learning in semi-supervised clustering. In: Proceedings of the International Conference on Machine Learning (2004)
Demiriz, A., Bennett, K., Embrechts, M.: Semi-supervised clustering using genetic algorithms. In: Artificial Neural Networks In Engineering (1999)
Dhillon, I.S., Modha, D.S.: Concept decompositions for large sparse text data using clustering. Machine Learning 42(1), 143–175 (2001)
Frigui, H., Nasraoui, O.: Unsupervised learning of prototypes and attribute weights. Pattern recognition 37(3), 567–581 (2004)
Jing, L., Ng, M.K., Xu, J., Huang, J.Z.: Subspace clustering of text documents with feature weighting K-Means algorithm. In: Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 802–812 (2005)
Rose, T., Stevenson, M., Whitehea, M.: The Reuters Corpus Volume 1– from yesterday’s news to tomorrow’s language resources. In: Proceedings of Third International Conference on Language Resources and Evaluation (2002)
Wagstaff, K., Cardie, C.: Clustering with instance-level constraints. In: Proceedings of the Seventeenth International Conference on Machine Learning (2000)
Xing, E.P., Ng, A.Y., Jordan, M., Russell, S.: Distance metric learning, with application to clustering with side-information. Advances in NIPSÂ 15 (2003)
Zhao, Y., Karypis, G.: Hierarchical clustering algorithms for document datasets. Data Mining and Knowledge Discovery 10, 141–168 (2005)
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Huang, R., Zhang, Z., Lam, W. (2006). Text Clustering with Limited User Feedback Under Local Metric Learning. In: Ng, H.T., Leong, MK., Kan, MY., Ji, D. (eds) Information Retrieval Technology. AIRS 2006. Lecture Notes in Computer Science, vol 4182. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11880592_11
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DOI: https://doi.org/10.1007/11880592_11
Publisher Name: Springer, Berlin, Heidelberg
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