Consensus-based ensembles of soft clusterings

Applied Artificial Intelligence

Volumn 22, Issue 7-8, 2008, Pages 780-810

  Punera, Kunal ^a   Ghosh, Joydeep ^a  

^a YAHOO RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

APPLICATIONS.; CLUSTERINGS; CONSENSUS ALGORITHMS; CONSENSUS METHOD; FUZZY C-MEANS; GRAPH PARTITIONING; MAXIMUM-LIKELIHOOD; REAL-LIFE DATASETS;

CHLORINE COMPOUNDS; CLUSTER ANALYSIS; FLOW OF SOLIDS; FUZZY CLUSTERING; GENETIC ALGORITHMS;

CLUSTERING ALGORITHMS;

EID: 49749128817     PISSN: 08839514     EISSN: 10876545     Source Type: Journal    
DOI: 10.1080/08839510802170546     Document Type: Conference Paper

References (38)

1. Ailon, N., M. Charikar, and A. Newman. 2005. Aggregating inconsistent information: Ranking and Clustering. In Proc. STOC'05. Baltimore, MD.
2. Banerjee, A., I. Dhillon, J. Ghosh, S. Merugu, and D. Modha. 2007. A generalized maximum entropy to Bregman co-clustering and matrix approximation. JMLR 8:1919-1986.
3. Bezdek, J. C. and S. K. Pal. 1992. Fuzzy Models for Pattern Recognition. Piscataway, NJ: IEEE Press.
4. Blake, C. L., and C. J. Merz. 1998. UCI Repository of machine learning databases, http://www.ics.uci.edu/~m.learn/MLRepository.html. Last accessed 12/2004.
5. Brelman, L. 1999. Combining predictors. In: Combining Artificial Neural, Nets, 31-50. ed., A. Sharkey, Secaucus, NJ: Springer-Verlag.
6. Dempster, A. P., N. M. Laird, and D. B. Rubin. 1977. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, 39, Series B:1-38.
7. Dhillon, I. S. 2001. Co-clustering documents and words using bipartite spectral graph partitioning. In: Proceedings of the Seventh ACMSIGKDD International Conference on Knowledge Discovery and Data Mining (KDD)., pp. 269-274
8. Dhillon, I. S., S. Mallela, and R. Kumar. 2003. A divisive information-theoretic feature clustering algorithm for text classification. Journal of Machine learning Research 3:1265-1287.
9. Dimitriadou, E., A. Weingessel, and K. Homik. Voting-merging: An ensemble method for clustering. In Proceedings of the International Conference on Artificial, Neural Networks (ICANN 01), eds. Georg Dorffher, Horst Bischof, and Kurt. Homik, Vienna, Austria, LNCS 2130, 217-224.
10. Dunn, J. C. 1973. A Fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters, Journal of Cybernetics 3:32-57.
11. Fern, X. Z. and C. E. Brodley. 2003. Random projection for high dimensional clustering: A cluster ensemble approach. In Proceedings of the Twentieth International Conference on Machine Learning. Washington, DC: ACM Press.
12. Fern, X. Z. and C. E. Brodley. 2004. Solving cluster ensemble problems by bipartite graph partitioning. In Proceedings of the Ttueniy-Firsl International, Conference on Machine, Learning. Banff, Alberta, Canada: ACM Press.
13. Fred, A. L. N. and A. K. Jain. 2001. Finding consistent clusters in data partitions. In Proceedings of the Third International Workshop on Multiple Classifier Systems, eds. J. Kittler and F. Roll, LNCS 2364, 309-318.
14. Fred, A. L. N. and A. K. Jain. 2002. Data clustering using evidence accumulation. In Proceedings of the Sixteenth International Conference on Pattern Recognition (ICPR), 276-280.
15. Freund, Y. and R. Schapire. 1996. Experiments with a New Boosting Algorithm. In Proceedings of the Thirteenth International Conference on Machine Learning. Morgan Kaufmann, Amherst, MA, 148-156.
16. Frossyniotis, D. S., M. Pertselakis, and A. Stafylopatis. 2002. A multi-clustering fusion algorithm. In Proceedings of the Second Hellenic Conference on AI, Springer-Verlag, Thessaloniki, Greece, 225-236.
17. Gablentz, W., M. Koppen, and E. Dimitriadou. 2000. Robust clustering by evolutionary computation. In Proc. Sth Online World Conference on Soft, Computing in Industrial Applications.
18. Ghosh, J. 2002. Multiclassifier systems: Back to the future. In Multiple Classifier Systems, eds. F. Roli and J. Kittler, LNCS 2364, Springer, 1-15.
19. Ghosh, J., A. Strehl, and S. Merugu. 2002. A consensus framework for integrating distributed clusterings under limited knowledge sharing. In Proceedings of NSF' Workshop on Next Generation Data Mining, 99-108.
20. Gionis, A., H. Mannila, and P. Tsaparas. 2005. Clustering aggregation. In Proc. ICDE.
21. Hadjitodorov, S. T., L. I. Kuncheva, and L. P. Todorova. 2006. Moderate diversity for better cluster ensembles. Information Fusion 7(3):264-275.
22. Hansen, L. and P. Salamon. 1990. Neural network ensembles. IEEE Transactions on Pattern Analysis and Machine Intelligence 12:993-1001.
23. Hubert, L. and P. Arabie. 1985. Comparing partitions. Journal of Classification 2:193-218.
24. Karypis, G., R. Aggarwal, V. Kumar, and S. Shekhar. 1997. Multilevel hypergraph partitioning: Application in VLSI domain. In Proceedings of the Thirty-Fourth Annual Conference on, Design Automation, 526-529.
25. Karypis, G. and V. Kumar. 1998. A fast and high quality multilevel scheme for partitioning irregular graphs. SIAM Journal on Scientific Computing 20(1):359-392.
26. Kreiger, A. M. and P. E. Green. 1999. A Generalized Rand-Index method for consensus clustering of separate partitions of the same data base. Journal of Classification 16:63-89.
27. Kullback, S. and R. A. Leibler. 1951. On information and sufficiency. Annals of Mathematical Statistics 22:79-86.
28. Kumar, S., J. Ghosh, and M. M. Crawford. 2001. Best basis feature extraction algorithms for classification of hyperspectral data. In IFFF, Transactions on Geoscience and Remote Sensing, SpI. Issue on Analysis of Hyperspectral Data. 39 (7) : 1368-1379.
29. Kuncheva, L. I. and S. T. Hadjitodorov. 2004. Using diversity in. cluster ensembles. In Proceedings of IEEE International Conference on Systems, Man and Cybernetics, 1214-1219.
30. Madeira, S. C. and A. L. Oliveira. 2004. Biclustering algorithms for biological data analysis: A survey. IEEE/ACM Transactions on Computational Biology and Bioinformatics 1:24-45.
31. Melville, P. and R. J. Mooney. 2003. Constructing diverse classifier ensembles using artificial training examples. In Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence (IJCAI), 505-510.
32. Merugu, S. and J. Ghosh. 2003. Privacy-preserving distributed clustering using generative models. In Proceedings of the Third IEEE International Conference on Data Mining (ICDM), 211-218.
33. Ng, A., M. Jordan, andY. Weiss. 2001. On spectral clustering: Analysis and an. algorithm. In Proceedings of Advances in Neural Information Processing Systems 14:849-856.
34. Opitz, D. and R. Maclin. 1999. Popular ensemble methods: An empirical study. Journal of Artificial, Intelligence Research 11:169-198.
35. Pedrycz, W. 2002. Collaborative fuzzy clustering. Pattern Recognition Letters 23:1675-1686.
36. Strehl, A. and J. Ghosh. 2002. Cluster ensembles - A knowledge reuse framework for combining multiple partitions. Journal on Machine Learning Research (JMLR) 3:583-617.
37. Topchy, A., A. Jain, and W. Punch. 2004. Mixture model for clustering ensembles. In Proceedings of the Fourth SIAM Conference, on Data Mining (SDM), 379-390.
38. Webb, G. I. 2000. Multiboosting: A. technique for combining boosting and wagging. Machine Learning 40:159-196.