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[21] I. S. Dhillon and D. S. Modha. Concept decompositions for large sparse
text data using clustering. Machine Learning , 42:143-175, 2001.
[22] B. E. Dom. An information-theoretic external cluster-validity measure.
Research Report RJ 10219, IBM, 2001.
[23] X. Fern and C. Brodley. Random projection for high dimensional data
clustering: A cluster ensemble approach. In Proceedings of 20th Inter-
national Conference on Machine Learning (ICML-2003) , 2003.
[24] Y. Freund and R. E. Schapire. Experiments with a new boosting algo-
rithm. In Lorenza Saitta, editor, Proceedings of the Thirteenth Inter-
national Conference on Machine Learning (ICML-96) , pages 148-156.
Morgan Kaufmann, July 1996.
[25] J. Ghosh and A. Strehl. Grouping Multidimensional Data: Recent Ad-
vances in Clustering , chapter Similarity-based Text Clustering: A Com-
paritive Study. Springer Berlin Heidelberg, 2006.
[26] M. Goldszmidt and M. Sahami. A probabilistic approach to full-text
document clustering. Technical Report ITAD-433-MS-98-044, SRI In-
ternational, 1998.
[27] T. Hertz, A. Bar-Hillel, and D. Weinshall. Boosting margin based dis-
tance functions for clustering. In Proceedings of 21st International Con-
ference on Machine Learning (ICML-2004) , 2004.
[28] M. Hiu, C. Law, A. Topchy, and A. K. Jain. Model-based clustering with
probabilistic constraints. In Proceedings of the 2005 SIAM International
Conference on Data Mining (SDM-05) , 2005.
[29] S. D. Kamvar, D. Klein, and C. D. Manning. Spectral learning. In Pro-
ceedings of the Eighteenth International Joint Conference on Artificial
Intelligence (IJCAI-2003) , pages 561-566, Acapulco, Mexico, 2003.
[30] M. Kearns, Y. Mansour, and A. Y. Ng. An information-theoretic analysis
of hard and soft assignment methods for clustering. In Proceedings of
UAI , pages 282-293, 1997.
[31] D. Klein, S. D. Kamvar, and C. Manning. From instance-level con-
straints to space-level constraints: Making the most of prior knowledge
in data clustering.
In Proceedings of ICML , pages 307-314, Sydney,
Australia, 2002.
[32] J. Kleinberg and E. Tardos. Approximation algorithms for classifica-
tion problems with pairwise relationships: Metric labeling and Markov
random fields. In Proceedings of FOCS , pages 14-23, 1999.
[33] B. Kulis, S. Basu, I. Dhillon, and R. J. Mooney. Semi-supervised graph
clustering: A kernel approach. Proceedings of 22nd International Con-
ference on Machine Learning (ICML-2005), 2005.
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