From cluster ensemble to structure ensemble

Information Sciences

Volumn 198, Issue , 2012, Pages 81-99

  Yu, Zhiwen ^a,b   You, Jane ^b   Wong, Hau San ^c   Han, Guoqiang ^a  

^a SOUTH CHINA UNIVERSITY OF TECHNOLOGY   (China)

^b HONG KONG POLYTECHNIC UNIVERSITY   (Hong Kong)

^c CITY UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

Cluster ensemble; Structure ensemble

Indexed keywords

CANCER GENE EXPRESSION; CLUSTER ENSEMBLES; CLUSTERING APPROACH; CLUSTERING SOLUTIONS; CORRELATION INDEX; DATA POINTS; DATA SETS; DATA SOURCE; MULTIPLE STRUCTURES; NORMALIZED CUTS; UCI REPOSITORY; UNIFIED STRUCTURE;

CLUSTERING ALGORITHMS; GENE EXPRESSION; GRAPH THEORY;

CONFORMAL MAPPING;

EID: 84859595994     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ins.2012.02.019     Document Type: Article

References (72)

1. A.A. Alizadeh, M.B. Eisen, R.E. Davis, C. Ma, I.S. Lossos, A. Rosenwald, J.G. Boldrick, H. Sabet, T. Tran, X. Yu, J.I. Powell, L.M. Yang, G.E. Marti, T. Moore, J. Hudson, L.S. Lu, D.B. Lewis, R. Tibshirani, G. Sherlock, W.C. Chan, T.C. Greiner, D.D. Weisenburger, J.O. Armitage, R. Warnke, R. Levy, W. Wilson, M.R. Grever, J.C. Byrd, D. Botstein, P.O. Brown, and L.M. Staudt Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling Nature 403 2000 503 511 (Pubitemid 30082188)
2. M.F. Amasyali, O. Ersoy, The performance factors of clustering ensembles, in: IEEE 16th Signal Processing, Communication and Applications Conference (SIU 2008), 2008, pp. 1-4.
3. A. Asuncion, D.J. Newman, UCI Machine Learning Repository, Irvine, CA: University of California, School of Information and Computer Science, 2007, < http://www.ics.uci.edu/mlearn/MLRepository.html >.
4. H. Ayad, M. Kamel, Finding natural clusters using multiclusterer combiner based on shared nearest neighbors, in: Proceedings of the Fourth International Workshop Multiple Classifier Systems, 2003.
5. H.G. Ayad, and M.S. Kamel Cumulative voting consensus method for partitions with variable number of clusters IEEE Transactions on Pattern Analysis and Machine Intelligence 30 1 2008 16 173
6. A. Bertoni, and G. Valentini Randomized maps for assessing the reliability of patients clusters in DNA microarray data analyses Artificial Intelligence in Medicine 37 2 2006 85 109 (Pubitemid 43816281)
7. A. Bertoni, and G. Valentini Discovering multi-level structures in bio-molecular data through the Bernstein inequality BMC Bioinformatics 9 Suppl. 2:S4 2008 1 9
8. L. Breiman Bagging predictors Machine Learning 24 2 1996 123 140 (Pubitemid 126724382)
9. L. Breiman Random forests Machine Learning 45 1 2001 5 32 (Pubitemid 32933532)
10. T. Dietterich Ensemble methods in machine learning Kittler Roli Multiple Classifier Systems Lecture Notes in Computer Science vol. 1857 2000 Springer 1 15
11. S. Dudoit, and J. Fridlyand A prediction-based resampling method to estimate the number of clusters in a dataset Genome Biology 3 7 2002 0036-1 0036-21
12. S. Dudoit, and J. Fridlyand Bagging to improve the accuracy of a clustering procedure Bioinformatics 19 2003 1090 1099 (Pubitemid 36790010)
13. X.Z. Fern, C.E. Brodley, Random projection for high dimensional data clustering: a cluster ensemble approach, in: Proceedings of the 20th International Conference on Machine Learning, 2003, pp. 186-193.
14. B. Fischer, and J.M. Buhmann Bagging for path-based clustering IEEE Transactions on Pattern Analysis and Machine Intelligence 25 11 2003 1411 1415
15. E.B. Fowlkes, and C.L. Mallows A method for comparing two hierarchical clusterings Journal of the American Statistical Association 78 1983 553 584
16. A.L.N. Fred, and A.K. Jain Combining multiple clusterings using evidence accumulation IEEE Transactions on Pattern Analysis and Machine Intelligence 27 6 2005 835 850 (Pubitemid 40889836)
17. Y. Freund, and R.E. Schapire A decision-theoretic generalization of on-line learning and an application to boosting Journal of Computer and System Sciences 55 1 1997 119 139 (Pubitemid 127433398)
18. J. Gllavata, E. Qeli, B. Freisleben, Detecting text in videos using fuzzy clustering ensembles, in: Eighth IEEE International Symposium on Multimedia (ISM'06), 2006, pp. 283-290.
19. D. Greene, A. Tsymbal, N. Bolshakova, P. Cunningham, Ensemble Clustering in Medical Diagnostics, Technical Report TCD-CS-2004-12, Dept. of Computer Science, Trinity College, Dublin, Ireland, 2004.
20. S.T. Hadjitodorov, L.I. Kuncheva, and L.P. Todorova Moderate diversity for better cluster ensembles Information Fusion 7 3 2006 264 275 (Pubitemid 43842123)
21. I. Hedenfalk, D. Duggan, Y. Chen, M. Radmacher, M. Bittner, R. Simon, P. Meltzer, B. Gusterson, M. Esteller, O.P. Kallioniemi, B. Wilfond, A. Borg, J. Trent, M. Raffeld, Z. Yakhini, A. Ben-Dor, E. Dougherty, J. Kononen, L. Bubendorf, W. Fehrle, S. Pittaluga, S. Gruvberger, N. Loman, O. Johannsson, H. Olsson, and G. Sauter Gene-expression profiles in hereditary breast cancer New England Journal of Medicine 344 8 2001 539 548 (Pubitemid 32167867)
22. T.K. Ho The random subspace method for constructing decision forests IEEE Transactions on Pattern Analysis and Machine Intelligence 20 8 1998 832 844 (Pubitemid 128741345)
23. Y. Hong, and S. Kwong Learning assignment order of instances for the constrained k-means clustering algorithm IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 39 2 2009 568 574
24. X. Hu, I. Yoo, Cluster ensemble and its applications in gene expression analysis, in: Proceedings of the Second Asia-Pacific Bioinformatics Conference, 2004, pp. 297-302.
25. X. Huang, X. Zheng, W. Yuan, F. Wang, and S. Zhu Enhanced clustering of biomedical documents using ensemble non-negative matrix factorization Information Sciences 181 11 2011 2293 2302
26. X. Hu, E.K. Park, and X. Zhang Microarray gene cluster identification and annotation through cluster ensemble and EM based informative textual summarization IEEE Transactions on Information Technology in Biomedicine 2009
27. A.K. Jain, and R.C. Dubes Algorithms for Clustering Data 1988 Prentice-Hall Englewood Cliffs, NJ
28. S. Kaski, J. Kangas, and T. Kohonen Bibliography of self-organizing map (SOM) papers: 1981-1997 Neural Computing Surveys 1 3&4 1998 1 176
29. J. Kittler, M. Hatef, R. P Duin, and J. Matas On combining classifiers IEEE Transactions on Pattern Analysis and Machine Intelligence 20 3 1998 226 239 (Pubitemid 128741312)
30. T. Kohonen Self-Organizing Maps second ed. 1997 Springer-Verlag Heidelberg
31. L.I. Kuncheva A theoretical study on six classifier fusion strategies IEEE Transactions on Pattern Analysis and Machine Intelligence 24 2 2002 281 286 (Pubitemid 34198215)
32. L.I. Kuncheva, and C.J. Whitaker Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy Machine Learning 51 2 2003 181 207
33. L.I. Kuncheva 'Fuzzy' vs. 'Non-fuzzy' in combining classifiers designed by boosting IEEE Transactions on Fuzzy Systems 11 6 2003 729 741
34. L.I. Kuncheva, and S.T. Hadjitodorov Using diversity in cluster ensembles SMC 2004 1214 1219 (Pubitemid 40408650)
35. L.I. Kuncheva, and Dmitry Vetrov Evaluation of Stability of k-means cluster ensembles with respect to random initialization IEEE Transactions on Pattern Analysis and Machine Intelligence 28 11 2006 1798 1808 (Pubitemid 46405427)
36. L.I. Kuncheva, and J.J. Rodríguez Classifier ensembles with a random linear oracle IEEE Transactions on Knowledge and Data Engineering 19 4 2007 500 508
37. L. Lam Classifier combinations: implementations and theoretical issues Kittler Roli Multiple Classifier Systems Lecture Notes in Computer Science vol. 1857 2000 Springer 78 86
38. T. Lange, and J.M. Buhmann Combining partitions by probabilistic label aggregation SIGKDD 2005 147 156 (Pubitemid 43224042)
39. Z. Lu, and H.H.S. Ip Generalized competitive learning of gaussian mixture models IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 39 4 2009 901 909
40. B. Minaei, A. Topchy, W. Punch, Ensembles of partitions via data resampling, in: Proceedings of International Conference on Information Technology: Coding and Computing, 2004, pp. 188-192.
41. S. Monti, P. Tamayo, J. Mesirov, and T. Golub Consensus clustering: a resamlping based method for class discovery and visualization of gene expression microarray data Journal of Machine Learning 52 2003 1 2
42. S. Ramaswamy, P. Tamayo, R. Rifkin, S. Mukherjee, C.-H. Yeang, M. Angelo, C. Ladd, M. Reich, E. Latulippe, J.P. Mesirov, T. Poggio, W. Gerald, M. Loda, E.S. Lander, and T.R. Golub Multi-class cancer diagnosis using tumor gene expression signatures Proceedings of the National Academy of Sciences 98 26 2001 15149 15154 (Pubitemid 34013984)
43. W.M. Rand Objective criteria for the evaluation of clustering methods Journal of the American Statistical Association 66 1971 846 850
44. J.J. Rodriguez, L.I. Kuncheva, and C.J. Alonso Rotation forest: a new classifier ensemble method IEEE Transactions on Pattern Analysis and Machine Intelligence 28 10 2006 1619 1630 (Pubitemid 46405081)
45. J.B. Shi, and J. Malik Normalized cuts and image segmentation IEEE Transactions on Pattern Analysis and Machine Intelligence 22 8 2000 888 905
46. M. Smolkin, and D. Ghosh Cluster stability scores for microarray data in cancer studies BMC Bioinformatics 4 2003 36
47. A. Strehl, and J. Ghosh Cluster ensembles - a knowledge reuse framework for combining multiple partitions Journal of Machine Learning Research 3 2002 583 617
48. A.I. Su, M.P. Cooke, K.A. Ching, Y. Hakak, J.R. Walker, T. Wiltshire, A.P. Orth, R.G. Vega, L.M. Sapinoso, A. Moqrich, A. Patapoutian, G.M. Hampton, P.G. Schultz, and J.B. Hogenesch Large-scale analysis of the human and mouse transcriptomes Proceedings of the National Academy of Sciences 99 7 2002 4465 4470 (Pubitemid 34286008)
49. A.P. Topchy, A.K. Jain, W. Punch, Combining multiple weak clusterings, in: Proceedings of IEEE International Conference on Data Mining, 2003, pp. 331-338.
50. A.P. Topchy, M.H.C. Law, A.K. Jain, and A.L.N. Fred Analysis of consensus partition in cluster ensemble ICDM 2004 2004 225 232 (Pubitemid 40731035)
51. A.P. Topchy, A.K. Jain, W. Punch, A mixture model for cluster ensembles, in: Proceedings of SIAM Conference on Data Mining, 2004, pp. 379-390.
52. A.P. Topchy, B.M. Bidgoli, A.K. Jain, W.F. Punch, Adaptive clustering ensembles, in: ICPR 2004, 2004, pp. 272-275.
53. A.P. Topchy, A.K. Jain, and W.F. Punch Cluster ensembles: models of consensus and weak partitions IEEE Transactions on Pattern Analysis and Machine Intelligence 27 12 2005 1866 1881 (Pubitemid 43051385)
54. A. Ulas, M. Semerci, O.T. Yildiz, and E. Alpaydin Incremental construction of classifier and discriminant ensembles Information Sciences 179 9 2009 1298 1318
55. G. Valentini Clusterv: a tool for assessing the reliability of clusters discovered in DNA microarray data Bioinformatics 22 3 2006 369 370 (Pubitemid 43205390)
56. G. Valentini Mosclust: a software library for discovering significant structures in bio-molecular data Bioinformatics 23 3 2007 387 389 (Pubitemid 46323167)
57. H. Wang, Z. Li, Y. Cheng, Distributed and parallelled EM algorithm for distributed cluster ensemble, Pacific-Asia Workshop on Computational Intelligence and Industrial Application (PACIIA'08), vol. 2, 2008, pp. 3-8.
58. R. Xia, C. Zong, and S. Li Ensemble of feature sets and classification algorithms for sentiment classification Information Sciences 181 6 2011 1138 1152
59. J. Xiao, C. He, X. Jiang, and D. Liu A dynamic classifier ensemble selection approach for noise data Information Sciences 180 18 2010 3402 3421
60. H. Xiong, J. Wu, and J. Chen K-means clustering versus validation measures: a data-distribution perspective IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 39 2 2009 318 331
61. L. Yang, H. Lv, and W. Wang Soft cluster ensemble based on fuzzy similarity measure IMACS Multiconference on Computational Engineering in Systems Applications 2 2006 1994 1997
62. H. Yin ViSOM - a novel method for multivariate data projection and structure visualization IEEE Transactions on Neural Networks 3 1 2002 237 243 (Pubitemid 34236851)
63. Z. Yu, H.S. Wong, and H. Wang Graph based consensus clustering for class discovery from gene expression data Bioinformatics 23 21 2007 2888 2896 (Pubitemid 350162895)
64. Z. Yu, Z. Deng, H.S. Wong, Identification of phosphorylation sites using a hybrid classifier ensemble approach, in: IEEE International Conference on Pattern Recognition 2008 (ICPR2008), Tampa, Florida, USA, 2008, pp. 1-4.
65. Z. Yu, Z. Deng, H.S. Wong, K-nearest neighbor classifier ensemble for prediction of phosphorylation sites, in: The 2008 International Conference on Bioinformatics and Computational Biology (BIOCOMP'08), Las Vegas, Nevada, USA, 2008, pp. 713-717.
66. Z. Yu, X. Wang, H.S. Wong. Ensemble based 3D human motion classification, in: IEEE International Joint Conference on Neural Networks (IJCNN2008), Hong Kong, China, 2008, pp. 506-510.
67. Z. Yu, H.S. Wong, Image Classification based on the bagging-adaboost ensemble, in; IEEE International Conference on Multimedia & Expo 2008 (ICME2008), Hannover, Germany, 2008, pp. 1481-1484.
68. Z. Yu, H.S. Wong, Knowledge based cluster ensemble for 3D head model classification, in: 19th International Conference on Pattern Recognition (ICPR 2008), 2008, pp. 1-4.
69. Z. Yu, and H.S. Wong Class discovery from gene expression data based on perturbation and cluster ensemble IEEE Transactions on NanoBioscience 2009
70. S. Zhai, B. Luo, Y. Guo, Fuzzy clustering ensemble based on dual boosting, in: Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007), vol. 2, 2007, pp. 240-244. (Pubitemid 351710748)
71. Z. Zhang, H. Cheng, S. Zhang, W. Chen, Q. Fang, Clustering aggregation based on genetic algorithm for documents clustering, IEEE World Congress on Evolutionary Computation (CEC 2008), 2008, pp. 3156-3161.
72. X. Zhang, L. Jiao, F. Liu, L. Bo, and M. Gong Spectral clustering ensemble applied to SAR image segmentation IEEE Transactions on Geoscience and Remote Sensing 46 7 2008 2126 2136