Ensemble clustering in the belief functions framework

International Journal of Approximate Reasoning

Volumn 52, Issue 1, 2011, Pages 92-109

  Masson, Marie Hélne ^a,c   Denoeux, Thierry ^b,c  

^a UNIVERSITÉ DE PICARDIE JULES VERNE   (France)

^b UNIVERSITÉ DE TECHNOLOGIE DE COMPIÈGNE   (France)

^c CNRS   (France)

Author keywords

Belief functions; Clustering; Ensemble clustering; Intervals of partitions; Lattice of partitions

Indexed keywords

BELIEF FUNCTION; CLUSTERING; ENSEMBLE CLUSTERING; INTERVALS OF PARTITIONS; LATTICE OF PARTITIONS;

CLUSTERING ALGORITHMS;

UNCERTAINTY ANALYSIS;

EID: 78649335185     PISSN: 0888613X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ijar.2010.04.007     Document Type: Conference Paper

References (39)

1. R. Avogadri, and G. Valentini Fuzzy ensemble clustering for DNA microarray data analysis Artificial Intelligence in Medicine 45 2-3 2009
2. J.-P. Barthélémy Monotone functions on finite lattices: an ordinal approach to capacities, belief and necessity functions J. Fodor, B. de Baets, P. Perny, Preferences and Decisions under Incomplete Knowledge 2000 Physica-Verlag 195 208
3. A. Ben-Hur, A. Elisseeff, and I. Guyon A stability based method for discovering structure in clustered data R.B. Aetman, Proceedings of the Pacific Symposium on Biocomputing 2002 New Jersey World Scientific Publishing Co. 6 17
4. J.C. Bezdek, J. Keller, R. Krisnapuram, and N.R. Pal Fuzzy Models and Algorithms for Pattern Recognition and Image Processing Series: The Handbooks of Fuzzy Sets vol. 4 1999 Kluwer Academic Publishers Norwell, MA, USA
5. D.L. Davies, and D.W. Bouldin A cluster separation measure IEEE Transactions on Pattern Analysis and Machine Intelligence 1 4 1979 224 227
6. W. Day Foreword: comparison and consensus of classifications Journal of Classification 3 1986 183 185
7. T. Denœux, and M.-H. Masson EVCLUS: EVidential CLUStering of proximity data IEEE Transactions on Systems, Man and Cybernetics Part B 34 1 2004 95 109
8. E. Dimitriadou, A. Weingessel, K. Hornik, Voting-Merging: an ensemble method for clustering, in: Proceedings of the International Conference on Artificial Neural Networks, ICANN'01, Vienna, Austria, 2001.
9. F.J. Duarte, A.L. Fred, A. Loureno, M.F. Rodrigues, Weighted evidence accumulation clustering, in: S.J. Simoff, G.J. Williams, J. Galloway, I. Kolyshkina (Eds.), Proceedings of the 4th Australasian Conf. Knowl. Discovery Data Mining, Sydney, NSW, Australia, University of Technology, Sydney, 2005, pp. 205-220.
10. S. Dudoit, and J. Fridlyand Bagging to improve the accuracy of a clustering procedure Bioinformatics 19 9 2003 1090 1099
11. J.C. Dunn Well separated clusters and optimal fuzzy partitions Journal of Cybernetics 4 1974 95 104
12. X.Z. Fern, and C.E. Broadley Random projection for high dimensional data clustering: a cluster ensemble approach T. Fawcett, N. Mishra, 2003 AAAI Press Washington, DC, USA 186 193
13. X.Z. Fern, and C.E. Broadley Solving cluster ensemble problems by bipartite graph partitioning C.E. Brodley, Proceedings of the 21th International Conference on Machine Learning (ICML 2004), Banff, AL, Canada 2004 ACM New York 281 288
14. A. Fred, A.K. Jain, Data clustering using evidence accumulation, in: Proceedings of the 16th International Conference on Pattern Recognition, Quebec, Canada, 2002, pp. 276-280.
15. A. Fred, and A.K. Jain Combining multiple clustering using evidence accumulation IEEE Transactions on Pattern Analysis and Machine Intelligence 27 2005 835 850
16. A. Fred, and A. Loureno Cluster ensemble methods: from single clusterings to combined solutions Studies in Computational Intelligence (SCI) 126 2008 3 30
17. M. Grabisch Belief functions on lattices International Journal of Intelligent Systems 24 1 2009 76 95
18. D. Greene, A. Tsymbal, N. Bolshakova, P. Cunningham, Ensemble clustering in medical diagnostics, in: Proceedings of the 17th IEEE Symposium on Computer-Based Medical Systems, Bethesda, MD, USA, 2004, pp. 576-581.
19. S.T. Hadjitodorov, L. Kuncheva, and L. Todorova Moderate diversity for better cluster ensemble Information Fusion 7 3 2006 264 275
20. K. Hornik, and F. Leisch Ensemble methods for cluster analysis A. Taudes, Adaptive Information Systems and Modelling in Economics and Management Science Interdisciplinary Studies in Economics and Management vol. 5 2005 Springer-Verlag 261 268
21. A. Hubert Comparing partitions Journal of Classification 2 1985 193 198
22. S. Le Hégarat-Mascle, I. Bloch, and D. Vidal-Madjar Application of Dempster-Shafer evidence theory to unsupervised classification in multisource remote sensing IEEE Transactions on Geoscience and Remote Sensing 35 4 1997 1018 1031
23. E. Levine, and E. Domany Resampling method for unsupervised estimation of cluster validity Neural Computation 13 2001 2573 2593
24. M.-H. Masson, and T. Denœux Clustering interval-valued data using belief functions Pattern Recognition Letters 25 2 2004 163 171
25. M.-H. Masson, and T. Denœux ECM: an evidential version of the fuzzy c-means algorithm Pattern Recognition 41 2008 1384 1397
26. M.-H. Masson, and T. Denœux Belief functions and cluster ensembles C. Sossai, G. Chemello, ECSQARU 2009 LNAI vol. 5590 2009 Springer-Verlag 323 334
27. S. Monti, P. Tamayo, J.P. Mesirov, and T.R. Golub Consensus clustering: a resampling-based method for class discovery and visualization of gene expression microarray data Machine Learning 52 2003 91 118
28. B. Montjardet The presence of lattice theory in discrete problems of mathematical social sciences. Why Mathematical Social Sciences 46 2003 103 144
29. D.A. Neumann, and V.T. Norton On lattice consensus methods Journal of Classification 3 1986 225 256
30. W. Rand Objective criteria for the evaluation of clustering methods Journal of the American Statistical Association 66 1971 846 850
31. P.J. Rousseeuw Silhouettes: a graphical aid to the interpretation and validation of cluster analysis Journal of Computational and Applied Mathematics 20 1987 53 65
32. V. Singh, L. Mukherjee, J. Peng, J. Xu, Ensemble clustering using semidefinite programming, Advances in neural information processing systems 20, in: Proceedings of the Twenty-First Annual Conference on Neural Information Processing Systems, Vancouver, British Columbia, Canada, December 3-6, 2007.
33. G. Shafer A Mathematical Theory of Evidence 1976 Princeton University Press Princeton, NJ
34. P. Smets Belief functions: the disjunctive rule of combination and the generalized Bayesian theorem International Journal of Approximate Reasoning 9 1993 1 35
35. P. Smets, and R. Kennes The transferable belief model Artificial Intelligence 66 1994 191 243
36. A. Strehl, and J. Ghosh Cluster ensemble - a knowledge reuse framework for combining multiple partitions Journal of Machine Learning Research 3 2002 563 618
37. A. Topchy, A.K. Jain, W. Punch, A mixture model for clustering ensembles, in: Proceedings of the 4th SIAM Conference on Data Mining (SDM'04), Lake Buena Vista, USA, 2004, pp. 279-390.
38. A. Topchy, A.K. Jain, and W. Punch Clustering ensembles: models of consensus and weak partitions IEEE Transactions on Pattern Analysis and Machine Intelligence 27 12 2005 1866 1881
39. K. Wagstaff, C. Cardie, S. Rogers, S. Schrdl, Constrained k-means clustering with background knowledge, in: Proceedings ICML 2001, Williamstown, USA, 2001, pp. 577-584.