R. Avogadri and G. Valentini, Fuzzy Ensemble Clustering for DNA Microarray Data Analysis, Artificial Intelligence in Medicine, vol.45, issue.2, 2009.
DOI : 10.1007/978-3-540-73400-0_68

J. Barthélémy, Monotone Functions on Finite Lattices: An Ordinal Approach to Capacities, Belief and Necessity Functions, Preferences and decisions under incomplete knowledge, pp.195-208, 2000.
DOI : 10.1007/978-3-7908-1848-2_11

A. Ben-hur, A. Elisseeff, and I. Guyon, A stability based method for discovering structure in clustered data, Biocomputing 2002, pp.6-17, 2002.
DOI : 10.1142/9789812799623_0002

J. C. Bezdek, J. Keller, R. Krisnapuram, and N. R. , Fuzzy Models and Algorithms for Pattern Recognition and Image Processing, Series: The Handbooks of Fuzzy Sets, 1999.

D. L. Davies and D. W. Bouldin, A Cluster Separation Measure, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.1, issue.2, pp.224-227, 1979.
DOI : 10.1109/TPAMI.1979.4766909

W. Day, Foreword: Comparison and consensus of classifications, Journal of Classification, vol.33, issue.2, pp.183-185, 1986.
DOI : 10.1007/BF01894187

T. Denoeux and M. Masson, EVCLUS: Evidential Clustering of Proximity Data, IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), vol.34, issue.1, pp.95-109, 2004.
DOI : 10.1109/TSMCB.2002.806496

E. Dimitriadou, A. Weingessel, and K. Hornik, Voting-Merging: An Ensemble Method for Clustering, Proc. of the International Conference on Artificial Neural Networks, ICANN'01, 2001.
DOI : 10.1007/3-540-44668-0_31

F. J. Duarte, A. L. Fred, A. Lourenço, M. F. Rodrigues, and M. , Weighted evidence accumulation clustering, Proc. of the 4th Australasian Conf Knowl Discovery Data Mining, pp.205-220, 2005.

S. Dudoit and J. Fridlyand, Bagging to improve the accuracy of a clustering procedure, Bioinformatics, vol.19, issue.9, pp.1090-1099, 2003.
DOI : 10.1093/bioinformatics/btg038

J. C. Dunn, Well-Separated Clusters and Optimal Fuzzy Partitions, Journal of Cybernetics, vol.4, issue.1, pp.95-104, 1974.
DOI : 10.1080/01969727408546059

X. Z. Fern and C. E. Broadley, Random projection for high dimensional data clustering: A cluster ensemble approach, Proc. of the 20th international conference on Machine Learning, pp.186-193, 2003.

X. Z. Fern and C. E. Broadley, Solving cluster ensemble problems by bipartite graph partitioning, Twenty-first international conference on Machine learning , ICML '04, pp.281-288, 2004.
DOI : 10.1145/1015330.1015414

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.58.6852

A. Fred and A. K. Jain, Data clustering using evidence accumulation, Object recognition supported by user interaction for service robots, pp.276-280, 2002.
DOI : 10.1109/ICPR.2002.1047450

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.2.3429

A. Fred and A. K. Jain, Combining Multiple Clusterings Using Evidence Accumulation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.27, issue.6, pp.835-850, 2005.
DOI : 10.1109/TPAMI.2005.113

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.87.9937

A. Fred and A. Lourenço, Cluster Ensemble Methods: from Single Clusterings to Combined Solutions, Studies in Computational Intelligence (SCI), vol.126, pp.3-30, 2008.
DOI : 10.1007/978-3-540-78981-9_1

M. Grabisch, Belief functions on lattices, International Journal of Intelligent Systems, vol.46, issue.2, pp.76-95, 2009.
DOI : 10.1002/int.20321

URL : https://hal.archives-ouvertes.fr/hal-00340378

D. Greene, A. Tsymbal, N. Bolshakova, and P. Cunningham, Ensemble clustering in medical diagnostics, Proceedings. 17th IEEE Symposium on Computer-Based Medical Systems, pp.576-581, 2004.
DOI : 10.1109/CBMS.2004.1311777

S. T. Hadjitodorov, L. Kuncheva, and L. Todorova, Moderate diversity for better cluster ensembles, Information Fusion, vol.7, issue.3, pp.264-275, 2006.
DOI : 10.1016/j.inffus.2005.01.008

K. Hornik and F. Leisch, Ensemble Methods for Cluster Analysis, Adaptive Information Systems and Modelling in Economics and Management Science of Interdisciplinary Studies in Economics and Management, pp.261-268, 2005.
DOI : 10.1007/3-211-29901-7_16

A. Hubert, Comparing partitions, Journal of Classification, vol.78, issue.1, pp.193-198, 1985.
DOI : 10.1007/BF01908075

A. K. Jain and R. C. Dubes, Algorithms for Clustering Data, 1988.

S. , L. 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, vol.35, issue.4, pp.1018-1031, 1997.

E. Levine and E. Domany, Resampling Method for Unsupervised Estimation of Cluster Validity, Neural Computation, vol.13, issue.11, pp.2573-2593, 2001.
DOI : 10.1109/TPAMI.1982.4767266

M. Masson and T. Denoeux, Clustering interval-valued proximity data using belief functions, Pattern Recognition Letters, vol.25, issue.2, pp.163-171, 2004.
DOI : 10.1016/j.patrec.2003.09.008

M. Masson and T. Denoeux, ECM: An evidential version of the fuzzy c-means algorithm, Pattern Recognition, vol.41, issue.4, pp.1384-1397, 2008.
DOI : 10.1016/j.patcog.2007.08.014

M. Masson and T. Denoeux, Belief Functions and Cluster Ensembles, LNAI, vol.158, issue.19, pp.323-334, 2009.
DOI : 10.1016/j.fss.2007.03.004

URL : https://hal.archives-ouvertes.fr/hal-00443737

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, pp.91-118, 2003.

B. Montjardet, The presence of lattice theory in discrete problems of mathematical social sciences. Why, Mathematical Social Sciences, vol.46, issue.2, pp.103-144, 2003.
DOI : 10.1016/S0165-4896(03)00072-6

D. A. Neumann and V. T. Norton, On lattice consensus methods, Journal of Classification, vol.46, issue.2, pp.225-256, 1986.
DOI : 10.1007/BF01894189

W. Rand, Objective Criteria for the Evaluation of Clustering Methods, Journal of the American Statistical Association, vol.15, issue.336, pp.846-850, 1971.
DOI : 10.1080/01621459.1963.10500845

P. J. Rousseeuw, Silhouettes: A graphical aid to the interpretation and validation of cluster analysis, Journal of Computational and Applied Mathematics, vol.20, pp.53-65, 1987.
DOI : 10.1016/0377-0427(87)90125-7

V. Singh, L. Mukherjee, J. Peng, and J. Xu, Ensemble clustering using semidefinite programming with applications, Proceedings of the Twenty-First Annual Conference on Neural Information Processing Systems, 2007.
DOI : 10.1007/s10994-009-5158-y

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3174015

G. Shafer and N. J. , A mathematical theory of evidence, 1976.

P. Smets, Belief functions: The disjunctive rule of combination and the generalized Bayesian theorem, International Journal of Approximate Reasoning, vol.9, issue.1, pp.1-35, 1993.
DOI : 10.1016/0888-613X(93)90005-X

P. Smets and R. Kennes, The transferable belief model, Artificial Intelligence, vol.66, issue.2, pp.191-243, 1994.
DOI : 10.1016/0004-3702(94)90026-4

URL : https://hal.archives-ouvertes.fr/hal-01185821

A. Strehl and J. Ghosh, Cluster ensemble -a knowledge reuse framework for combining multiple partitions, Journal of Machine Learning Research, vol.3, pp.563-618, 2002.

A. Topchy, A. K. Jain, and W. Punch, A Mixture Model for Clustering Ensembles, Proc. of the 4th SIAM Conference on Data Mining (SDM'04), pp.279-390, 2004.
DOI : 10.1137/1.9781611972740.35

A. Topchy, A. K. Jain, and W. Punch, Clustering ensembles: models of consensus and weak partitions, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.27, issue.12, pp.1866-1881, 2005.
DOI : 10.1109/TPAMI.2005.237

K. Wagstaff, C. Cardie, S. Rogers, and S. Schrdl, Constrained kmeans clustering with background knowledge, Proceedings ICML 2001, pp.577-584, 2001.