Assisting users with clustering tasks by combining metric learning and classification

Proceedings of the National Conference on Artificial Intelligence

Volumn 1, Issue , 2010, Pages 394-400

  Basu, Sumit ^a   Fisher, Danyel ^a   Drucker, Steven M ^a   Lu, Hao ^b  

^a MICROSOFT RESEARCH   (United States)

^b UNIVERSITY OF WASHINGTON   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; LEARNING ALGORITHMS;

CLUSTERINGS; DISTANCE METRICS; HUMAN SUBJECTS; HYBRID MECHANISMS; METRIC LEARNING; TRADITIONAL APPROACHES;

CLUSTERING ALGORITHMS;

EID: 77958594580     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (13)

1. Aldous, D. 1983. "Exchangeability and Related Topics", in l'École d'été de probabilités de Saint-Flour, XIII-I983, Berlin: Springer, pp 1-198.
2. Bauer, E. and Kohavi, R. 1999. An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants. Machine Learning 36(1-2): 106-139.
3. Berry, M. W., Dumais, S.T., and O'Brien, G.W. 1995. Using Linear Algebra for Intelligent Information Retrieval. SIAM Review 37(4): 573-595.
4. Bilenko, M., Basu, S., and Mooney, R.J-. 2004. Integrating Constraints and Metric Learning in Semi-Supervised Clustering. In Proceedings of the Int'l Conf. on Machine Learning (ICML).
5. Bishop, C. M. 2006. Pattern Recognition and Machine Learning. New York: Springer.
6. Cohn, D., and Caruana, R. 2000. Semi-Supervised Clustering: Incorporating User Feedback to Improve Cluster Utility. In Proceedings of the Conf. on Artificial Intelligence. AAAI Press.
7. Desjardins, M., MacGlashan, J., and Ferraioli, J. 2007. Interactive Visual Clustering. In Proceedings of the Int'l Conf. on Intelligent User Interfaces. ACM Press.
8. Engelmore, R., and Morgan, A. eds. 1986. Blackboard Systems. Reading, Mass.: Addison-Wesley.
9. Freund, Y., Iyer, R., Schapire, R.E., and Singer, Y. 2003. An Efficient Boosting Algorithm for Combining Preferences. Journal of Machine Learning Research 4: 933-969.
10. Ng, A. 2004. Feature Selection, LI vs. L2 Regularization, and Rotational Invariance. In Proceedings of the Int'l. Conf. on Machine Learning (ICML).
11. Nocedal, J. "Updating Quasi-Newton Matrices with Limited Storage." Mathematics of Computation 35: 773-782.
12. Salton, G. and McGill, M.J. 1983. Introduction to Modern Information Retrieval. New York: McGraw-Hill.
13. Seifert, C. and Lex, E. 2009. A Novel Visualization Approach for Data-Mining-Related Classification. In Proceedings of Information Visualization.