Cluster ensemble selection

Society for Industrial and Applied Mathematics - 8th SIAM International Conference on Data Mining 2008, Proceedings in Applied Mathematics 130

Volumn 2, Issue , 2008, Pages 787-797

  Fern, Xiaoli Z ^a   Lin, Wei ^a  

^a Oregon State University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLUSTER ENSEMBLES; CLUSTERING SOLUTIONS; ENSEMBLE SELECTIONS; RANDOM SELECTION;

DATA MINING;

EID: 52649090203     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1137/1.9781611972788.71     Document Type: Conference Paper

References (20)

1. C. L. Blake and C. J. Merz. UCI repository of machine learning databases, 1998.
2. R. Caruana, A. Munson, and A. Niculescu-Mizil. Getting the most out of ensemble selection. In Proceedings of the Sixth international Conference on Data Mining, 2006.
3. R. Caruana, A. Niculescu-Mizil, G. Crew, and A. Ksikes. Ensemble selection from libraries of models. In Proceedings of the twenty first International Conference on Machine Learning, 2004.
4. J. Dy, C. E. Brodley, A. Kak, C. Shyu, and L. S. Broderick. The customized queries approach to cbir using em. In Proc. of IEEE Conference on Computer Vision and Pattern Recognition, pages 400-406, 1999.
5. X. Z. Fern and C. E. Brodley. Random projection for high dimensional data clustering: A cluster ensemble approach. In Proceedings of the Twentieth International Conference on Machine Learning, pages 186-193, 2003.
6. X. Z. Fern and C. E. Brodley. Solving cluster ensemble problems by bipartite graph partitioning. In Proceedings of the Twenty First International Conference on Machine Learning, pages 281-288, 2004.
7. A. L. N. Fred and A. K. Jain. Data clustering using evidence accumulation. In Proceedings of International Conference on Pattern Recognition, 2002.
8. M. Friedl, D. McIver, J. Hodges, X. Zhang, D. Muchoney, A. Strahler, C. Woodcock, S. Gopal, A. Schneider, A. Cooper, A. Baccini, F. Gao, and C. Schaaf. Global land cover mapping from modis: algorithms and early results. Remote Sensing of Environment, 83:287-302, 2002.
9. D. Greene, A. Tsymbal, N. Bolshakova, and P. Cunningham. Ensemble clustering in medical diagnostics. In Proc. 17th IEEE Symp. on Computer-Based Medical Systems, 2004.
10. S. T. Hadjitodorov, L. I. Kuncheva, and L. P. Todorova. Moderate diversity for better cluster ensembles. Information Fusion, 7(3):264-275, 2006.
11. L. Kuncheva and S.T. Hadjitodorov. Using diversity in cluster ensembles. In Proceedings of IEEE Int. Conf. on Systems, Man and Cybernetics, 2004.
12. J. MacQueen. Some methods for classifications and analysis of multivariate observations. In Proc. Symp. Mathematical Statistics and Probability, 5th, 1967.
13. D. Margineantu and T. Dietterich. Pruning adaptive boosting. In Proc. 14th International Conference on Machine Learning, pages 211-218, 1997.
14. S. Monti, P Tamayo, J. Mesirov, and T. Golub. Consensus clustering: A resampling-based method for class discovery and visualization of gene expression microarray data. Machine Learning, 52:91-118, 2003.
15. D. A. Neumann and V. T. Norton. Clustering and isolation in the consensus problem for partitions. Journal of Classification, 3:281-298, 1986.
16. A. Ng, M. Jordan, and Y. Weiss. On spectral clustering: Analysis and an algorithm. In Advances in Neural Information Processing Systems 14, pages 849-856, 2002.
17. A. Strehl and J. Ghosh. Cluster ensembles - a knowledge reuse framework for combining multiple partitions. Machine Learning Research, 3:583-417, 2002.
18. A. Topchy, A. K. Jain, and W. Punch. Combining multiple weak clusterings. In Proceedings IEEE International Conference on Data Mining, pages 331-338, 2003.
19. A. Topchy, A. K. Jain, and W. Punch. A mixture model for clustering ensembles. In Proceedings of SIAM Conference on Data Mining, pages 379-390, 2004.
20. A. Topchy, A.K. Jain, and W. Punch. Clustering ensembles: Models of consensus and weak partition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(12):1866-1881, 2005.