Learning pairwise similarity for data clustering

Proceedings - International Conference on Pattern Recognition

Volumn 1, Issue , 2006, Pages 925-928

  Fred, Ana L N ^a   Jain, Anil K ^b  

^a INSTITUTO SUPERIOR TÉCNICO   (Portugal)

^b Michigan State University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

FEATURE EXTRACTION; KNOWLEDGE ENGINEERING; LEARNING ALGORITHMS; PROBLEM SOLVING;

CLUSTERING PROBLEM; CLUSTERING TECHNIQUES; DATA POINTS; PARAMETER CONFIGURATIONS;

CLUSTERING ALGORITHMS;

EID: 34047201719     PISSN: 10514651     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICPR.2006.754     Document Type: Conference Paper

References (9)

1. H. Ayad and M. Kamel. Cluster-based cumulative ensembles. In N. Oza and R. Polikar, editors, Proc. the 6th International Workshop on Multiple Classifier Systems, pages 236-245. LCNS 3541, 2005.
2. C. B. D.J. Newman, S. Hettich and C. Merz. UCI repository of machine learning databases, 1998.
3. A. Fred and A. K. Jain. Robust data clustering. In Proc. of IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2003, Madison, June 2003.
4. A. L. Fred and A. K. Jain. Combining multiple clusterings using evidence accumulation. IEEE Trans. on Pattern Analysis and Machine Intelligence, 27(6): 835-850, 2005.
5. A. Jain, M. N. Murty, and P. Flynn. Data clustering: A review. ACM Computing Surveys, 31(3):264-323, September 1999.
6. L. I. Kuncheva and S. Hadjitodorov. Using diversity in cluster ensembles. In Proc. of IEEE Intl. Conference on Systems, Man and Cybernetics, pages 1214-1219, 2004.
7. M. H. C. Law, A. P. Topchy, and A. K. Jain. Multiobjective data clustering. In Proc. of IEEE Conference on Computer Vision and Pattern Recognition, volume 2, pages 424-430, Washington D.C., 2004.
8. A. Y. Ng, M. I. Jordan, and Y. Weiss. On spectral clustering: Analysis and an algorithm. In T. G. Dietterich, S. Becker, and Z. Ghahramani, editors, Advances in Neural Information Processing Systems 14, Cambridge, MA, 2002. MIT Press.
9. A. Strehl and J. Ghosh. Cluster ensembles - a knowledge reuse framework for combining multiple partitions. Journal of Machine Learning Research, 3(Dec):583-617, 2003.