Nonparametric Bayesian co-clustering ensembles

Proceedings of the 11th SIAM International Conference on Data Mining, SDM 2011

Volumn , Issue , 2011, Pages 331-342

  Wang, Pu ^a   Laskey, Kathryn B ^a   Domeniconi, Carlotta ^a   Jordan, Michael I ^b  

^a George Mason University   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN NETWORKS; CLUSTERING ALGORITHMS; INFORMATION DISSEMINATION;

CLUSTERING ENSEMBLE; COLUMN CLUSTERS; DIRICHLET PROCESS; EMPIRICAL EVALUATIONS; NESTED PARTITIONS; NON-PARAMETRIC BAYESIAN; PRIOR DISTRIBUTION; ROBUST CONSENSUS;

DATA MINING;

EID: 84880124268     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1137/1.9781611972818.29     Document Type: Conference Paper

References (21)

1. C. E. Antoniak. Mixtures of dirichlet processes with applications to bayesian nonparametric problems. The Annals of Statistics, 2(6):1152-1174, 1974.
2. D. Blackwell and J. B. Macqueen. Ferguson distributions via pólya urn schemes. The Annals of Statistics, 1:353-355, 1973.
3. D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent Dirichlet allocation. Journal of Machine Learning Research, 3:993-1022, 2003.
4. I. S. Dhillon, S. Mallela, and D. S. Modha. Information-theoretic co-clustering. In KDD '03: Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 89-98, New York, NY, 2003. ACM.
5. T. S. Ferguson. A Bayesian analysis of some nonparametric problems. The Annals of Statistics, 1(2): 209-230, 1973.
6. X. Fern and C. Brodley. Solving cluster ensemble problems by bipartite graph partitioning. In International Conference on Machine Learning, pages 281-288, 2004.
7. A. Fred and A. Jain. Combining multiple clusterings using evidence accumulation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(6):835-850, 2005.
8. A. Gelman, J. B. Carlin, H. S. Stern, and D. B. Rubin. Bayesian Data Analysis, Second Edition (Chapman & Hall/CRC Texts in Statistical Science). Chapman and Hall/CRC, 2 edition, 2003.
9. P. J. Green. Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika, 82(4):711-732, 1995.
10. F. Gullo, C. Domeniconi, and A. Tagarelli. Projective clustering ensembles. In IEEE International Conference on Data Mining, pages 794-799, 2009.
11. L. Kuncheva, S. Hadjitodorov, and L. Todorova. Experimental comparison of cluster ensemble methods. In International Conference on Information Fusion, pages 1-7, 2006.
12. E. Meeds and S. Roweis. Nonparametric Bayesian biclustering. Technical Report UTML TR 2007-001, Department of Computer Science, University of Toronto, 2007.
13. R. M. Neal. Markov chain sampling methods for dirich-let process mixture models. Journal of Computational and Graphical Statistics, 9(2):249-265, 2000.
14. O. Papaspiliopoulos and G. O. Roberts. Retrospective markov chain monte carlo methods for dirichlet process hierarchical models. Biometrika, 95(1):169-186, March 2008.
15. D. M. Roy and Y. W. Teh. The Mondrian process. In Advances in Neural Information Processing Systems (NIPS), volume 21, 2008.
16. J. Sethuraman. A constructive definition of dirichlet priors. Statistica Sinica, 4:639-650, 1994.
17. M. Shafiei and E. Milios. Latent Dirichlet co-clustering. In IEEE International Conference on Data Mining, pages 542-551, 2006.
18. H. Shan and A. Banerjee. Bayesian co-clustering. In IEEE International Conference on Data Mining (ICDM), 2008.
19. A. Strehl and J. Ghosh. Cluster ensembles-A knowledge reuse framework for combining multiple partitions. J. Mach. Learn. Res., 3:583-617, 2003.
20. A. Topchy, A. Jain and W. Punch. A mixture model for clustering ensembles. In SIAM International Conference on Data Mining, pages 379-390, 2004.
21. P. Wang, C. Domeniconi, and K. Laskey. Latent Dirichlet Bayesian co-clustering. In Proceedings of the European Conference on Machine Learning, volume 5782, pages 522-537. Springer Berlin Heidelberg, 2009.