Cluster ensembles

Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery

Volumn 1, Issue 4, 2011, Pages 305-315

  Ghosh, Joydeep ^a   Acharya, Ayan ^b  

^a The University of Texas at Austin   (United States)

^b NONE

Author keywords

[No Author keywords available]

Indexed keywords

CLUSTERING ALGORITHMS;

CLUSTER ENSEMBLES; CLUSTERING APPROACH; CONSENSUS CLUSTERING; CONSENSUS SOLUTIONS; MULTIPLE CLUSTERINGS; STABLE CLUSTERING; UNIQUE FEATURES;

CLUSTER ANALYSIS;

EID: 80053039117     PISSN: 19424787     EISSN: 19424795     Source Type: Journal    
DOI: 10.1002/widm.32     Document Type: Article

References (52)

1. Sharkey A. Combining Artificial Neural Nets. Secaucus, NJ, USA: Springer-Verlag, New York, Inc.; 1999.
2. Tumer K, Ghosh J. Robust order statistics based ensembles for distributed data mining. In: Hillol Kargupta H, Chan P, eds, Advances in Distributed and Parallel Knowledge Discovery. AAAI Press; 2000, 85-110.
3. Kuncheva LI. Combining Pattern Classifiers: Methods and Algorithms. Hoboken, NJ: John Wiley & Sons; 2004.
4. Ayad HG, Kamel MS. Cumulative voting consensus method for partitions with variable number of clusters. IEEE Trans Pattern Anal Mach Intell 2008, 30:160-173.
5. Hore P, Hall LO, Goldgof DB. A scalable framework for cluster ensembles. Pattern Recognit 2009, 42:676-688.
6. Fred A, Jain AK. Combining multiple clusterings using evidence accumulation. IEEE Trans Pattern Anal Mach Intell 2005, 27:835-850.
7. Strehl A, Ghosh J. Cluster ensembles-a knowledge reuse framework for combining multiple partitions. J Mach Learn Res 2002, 3:583-617.
8. Kuncheva LI, Hadjitodorov ST. Using diversity in cluster ensemble. IEEE Int Conf Syst Man Cybern 2004, 2:1214-1219.
9. Hu X, Yoo I. Cluster ensemble and its applications in gene expression analysis. In: APBC '04: Proceedings of the second conference on Asia-Pacific bioinformatics, Darlinghurst, Australia: Australian Computer Society, Inc; 2004.
10. Karypis G, Han E-H, Kumar V. Chameleon: hierarchical clustering using dynamic modeling. IEEE Comput 1999, 32:68-75.
11. Sevillano X, Cobo G, Alías F, Socoró JC. Feature diversity in cluster ensembles for robust document clustering. In: SIGIR '06: Proceedings of the 29th Annual International ACM SIGIR Conference on Research andDevelopment in Information Retrieval, New York: ACM; 2006, 697-698.
12. Ghosh J, Strehl A, Merugu S. A consensus framework for integrating distributed clusterings under limited knowledge sharing. In: Proceedings of NSF Workshop on Next Generation Data Mining, Baltimore; 2002, 99-108.
13. Asur S, Parthasarathy S, Ucar D. An ensemble framework for clustering protein-protein interaction networks. In: Proceedings of 15th Annual International Conference on Intelligent Systems for Molecular Biology (ISMB); 2007, 29-40.
14. Brodley CE. Random projection for high dimensional data clustering, a cluster ensemble approach In: Proceedings of 20th International Conference on Machine Learning (ICML'03), Washington, DC; 2003.
15. Merugu S, Ghosh J. A distributed learning framework for heterogeneous data sources. In: Proc. KDD; 2005, 208-217.
16. Mirkin B. Mathematical Classification and Clustering. Dordrecht: Kluwer; 1996.
17. Day WHE. Foreword: comparison and consensus of classifications. J. Classi 1986, 3:183-185.
18. Topchy A, Jain A, Punch W. A mixture model for clustering ensembles. In: Proceedings of SIAM International Conference on Data Mining; 2004, 379-390.
19. Wang H, Shan H, Banerjee A. Bayesian cluster ensembles. In: Proceedings of the Ninth SIAM International Conference on Data Mining; 2009, 211-222.
20. Hubert L, Arabie P. Comparing partitions. J Classif 1985, 2:193-218.
21. Meila M. Comparing clusterings by the variation of information. In: Proceedings of Conference on Learning Theory; 2003, 173-187.
22. Wu J, Chen J, Xiong H, Xie M. External validation measures for k-means clustering: a data distribution perspective. Expert Syst Appl 2009, 36:6050-6061.
23. Topchy AP, Law MHC, Jain AK, Fred AL. Analysis of consensus partition in cluster ensemble. In: ICDM '04: Proceedings of the Fourth IEEE International Conference on Data Mining, Washington, DC: IEEE Computer Society; 2004, 225-232.
24. Hilton P, Pedersen J, Stigter J. On partitions, surjections and stirling numbers. In: Bull Belgian Math Soc 1994, 1:713-725, 1994.
25. Topchy A, Jain AK, Punch W. Combining multiple weak clusterings. In: ICDM '03: Proceedings of the Third IEEE International Conference on Data Mining; page 331, Washington, DC: IEEE Computer Society; 2003, 331
26. Bishop CM. Pattern Recognition and Machine Learning. Secaucus, NJ, USA: Springer, New York, Inc.; 2006.
27. Wang P, Domeniconi C, Laskey K. Nonparametric bayesian clustering ensembles. In: Machine Learning and Knowledge Discovery in Databases. Lecture Notes in Computer Science, Vol. 6323, Ch. 28. Berlin/ Heidelberg: Springer; 2010.
28. Karypis G, Kumar V. A fast and high quality multilevel scheme for partitioning irregular graphs. SIAM J Sci Comput 1998; 20:359-392.
29. Strehl A, Ghosh J. A scalable approach to balanced, high-dimensional clustering of market-baskets. In: Proc. HiPC 2000, Bangalore, LNCS, Vol. 1970. Springer; 2000.
30. Punera K, Ghosh J. Consensus based ensembles of soft clusterings. In: Proc.MLMTA'07 -International Conference onMachine Learning:Models, Technologies & Applications; 2007.
31. Al Razgan M, Domeniconi C. Weighted cluster ensemble. In: Proceedings of SIAM International Conference on Data Mining; 2006, 258-269.
32. Monti S, Tamayo P, Mesirov J, Golub T. Consensus clustering-a resampling-based method for class discovery and visualization of gene expression microarray data. J Mach Learn 2003, 52:91-118.
33. Nguyen N, Caruana R. Consensus clusterings. In: Proceedings of International Conference on Data Mining; 2007, 607-612.
34. Zhong S, Ghosh J. A unified framework for modelbased clustering. J Mach Learn Res 2003, 4:1001-1037.
35. Li T, Ding C, Jordan M. Solving consensus and semisupervised clustering problems using non-negative matrix factorization. In: Proceedings of Eighth IEEE International Conference on Data Mining; 2007, 577-582.
36. Swift S, Tucker A, Vinciotti V, Martin M, Orengo C, Liu X, Kellam P. Consensus clustering and functional interpretation of gene-expression data. Genome Biol 2004, 5:R94.
37. Wang F, Wang X, Li T. Generalized cluster aggregation. In: IJCAI'09: Proceedings of the 21st International Jont Conference on Artifical Intelligence, San Francisco, CA: Morgan Kaufmann Publishers Inc.; 2009, 1279-1284.
38. Banerjee A, Merugu S, Dhillon I, Ghosh J. Clustering with Bregman divergences. J. Mach Learn Res 2005, 6:1705-1749.
39. Li T, Ding C. Weighted consensus clustering. In: Proceedings of Eighth SIAM International Conference on Data Mining; 2008, 798-809.
40. Lee DD, Seung HS. Algorithms for non-negativematrix factorization. In: NIPS. Denver, CO, USA: MIT Press; 2000.
41. Goder A, Filkov V. Consensus clustering algorithms: Comparison and refinement. In: Proceedings of the Tenth Workshop on Algorithm Engineering and Experiments; 2008, 109-117.
42. Gionis A, Mannila H, Tsaparas P. Clustering aggregation. ACM Trans Knowl Discov Data 2007, 1:109-117.
43. Bansal N, Blum AL, Chawla S. Correlation clustering. In: Proceedings of Foundations of Computer Science; 2002, 238-247.
44. Karypis G, Aggarwal R, Kumar V, Shekhar S. Multilevel hypergraph partitioning: applications in VLSI domain. In: Proceedings of the Design and Automation Conference; 1997, 526-529.
45. Fern X, Brodley C. Solving cluster ensemble problems by bipartite graph partitioning. In: Proceedings of International Conference on Machine Learning; 2004, 281-288.
46. Dudoit S, Fridlyand J. Bagging to improve the accuracy of a clustering procedure. Bioinformatics 2003, 19:1090-1099.
47. Yoon SY Ahn, SH Lee, SB Cho, JH Kim. Heterogeneous clustering ensemble method for combining different cluster results. In: Proceedings of BioDM 2006, Lecture Notes in Computer Science, Vol. 3916; 2006, 82-92.
48. Yang Y, KamelMS. An aggregated clustering approach using multi-ant colonies algorithms. Pattern Recognit 2006, 39:1278-1289.
49. Deodhar M, Ghosh J. Consensus clustering for detection of overlapping clusters in microarray data. In: ICDMW '06: Proceedings of the Sixth IEEE International Conference on Data Mining Workshops, Washington, DC: IEEE Computer Society; 2006, 104-108.
50. He Z, Xu X, Deng S. A cluster ensemble method for clustering categorical data. Inform Fusion 2005, 6:143-151.
51. Ye Y, Li T, Chen Y, Jiang Q. Automatic malware categorization using cluster ensemble. In: KDD '10: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, New York: ACM; 2010, 95-104.
52. Chiu T-Y, Hsu T-C, Wang J-S. Ap-based consensus clustering for gene expression time series. In: International Conference on Pattern Recognition; 2010, 2512-2515.