.. Approches-de-type-ensemble-de-clusterings, 149 4.3.1 Clustering consensus par ensemble de clusterings, p.151

. Bibliographie and . Achtert, Clustering multirepresented objects using combination trees, Lecture Notes in Computer Science, vol.3918, pp.174-178, 2006.

H. Aikake, Information theory and an extension of the maximum likelihood principle, Proceedings of 2nd International Symposium on Information Theory, pp.267-281, 1973.

M. Aupetit, Learning topology with the generative gaussian graph and the em algorithm, Advances in Neural Information Processing Systems, p.2006, 2006.

B. Bae, E. Bae, and J. Bailey, COALA: A Novel Approach for the Extraction of an Alternate Clustering of High Quality and High Dissimilarity, Sixth International Conference on Data Mining (ICDM'06), pp.53-62, 2006.
DOI : 10.1109/ICDM.2006.37

. Basu, Active Semi-Supervision for Pairwise Constrained Clustering, SDM. SIAM, 2004.
DOI : 10.1137/1.9781611972740.31

J. Bekkerman, R. Bekkerman, and J. Jeon, Multi-modal Clustering for Multimedia Collections, 2007 IEEE Conference on Computer Vision and Pattern Recognition, 2007.
DOI : 10.1109/CVPR.2007.383223

. Bekkerman, Combinatorial Markov Random Fields, Proceedings of ECML-06, the 17th European Conference on Machine Learning, pp.30-41, 2006.
DOI : 10.1007/11871842_8

J. C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms, 1981.
DOI : 10.1007/978-1-4757-0450-1

S. Bickel and T. Scheffer, Multi-View Clustering, Fourth IEEE International Conference on Data Mining (ICDM'04), pp.19-26, 2004.
DOI : 10.1109/ICDM.2004.10095

S. Bickel and T. Scheffer, Estimation of Mixture Models Using Co-EM, 16th European Conference on Machine Learning ECML 2001, pp.35-46, 2005.
DOI : 10.1007/11564096_9

C. Biernacki, Pourquoi les modèles de mélange pour la classification ?, pp.1-22, 2009.

M. Blum, A. Blum, and T. Mitchell, Combining labeled and unlabeled data with co-training, Proceedings of the eleventh annual conference on Computational learning theory , COLT' 98, 1998.
DOI : 10.1145/279943.279962

. Celeux, . Govaert, G. Celeux, and G. Govaert, A classification EM algorithm for clustering and two stochastic versions, Computational Statistics & Data Analysis, vol.14, issue.3, pp.315-332, 1992.
DOI : 10.1016/0167-9473(92)90042-E

URL : https://hal.archives-ouvertes.fr/inria-00075196

H. Chang and D. Yeung, Locally linear metric adaptation for semisupervised clustering, Proceedings of the twenty-first international conference on Machine learning, ICML '04, p.20, 2004.

. Cleuziou, CoFKM: A Centralized Method for Multiple-View Clustering, 2009 Ninth IEEE International Conference on Data Mining, pp.752-757, 2009.
DOI : 10.1109/ICDM.2009.138

URL : https://hal.archives-ouvertes.fr/hal-00460800

X. H. Dang and J. Bailey, Generation of Alternative Clusterings Using the CAMI Approach, SDM, pp.118-129, 2010.
DOI : 10.1137/1.9781611972801.11

B. Davidson, I. Davidson, and S. Basu, A survey of clustering with instance level constraints, In ACM Transactions on Knowledge Discovery from Data, pp.1-41, 2007.

Q. Davidson, I. Davidson, and Z. Qi, Finding Alternative Clusterings Using Constraints, 2008 Eighth IEEE International Conference on Data Mining, pp.773-778, 2008.
DOI : 10.1109/ICDM.2008.141

R. Davidson, I. Davidson, and S. S. Ravi, Agglomerative Hierarchical Clustering with Constraints: Theoretical and Empirical Results, PKDD, pp.59-70, 2005.
DOI : 10.1007/11564126_11

R. Davidson, I. Davidson, and S. S. Ravi, -Means Algorithm, SDM, 2005.
DOI : 10.1137/1.9781611972757.13

URL : https://hal.archives-ouvertes.fr/in2p3-00857367

. Dempster, Maximum Likelihood from Incomplete Data via the EM Algorithm, Journal of Royal Statistical Society B, vol.39, pp.1-38, 1977.

. Dhillon, A unified view of kernel k-means, spectral clustering and graph cuts, 2005.

. Ding, On the Equivalence of Nonnegative Matrix Factorization and Spectral Clustering, Proc. SIAM Data Mining Conf, pp.606-610, 2005.
DOI : 10.1137/1.9781611972757.70

S. Dantas, S. De-carvalho-]-dos, A. B. Dantas, and F. De-carvalho, Adaptive Batch SOM for Multiple Dissimilarity Data Tables, 2011 IEEE 23rd International Conference on Tools with Artificial Intelligence, pp.575-578, 2011.
DOI : 10.1109/ICTAI.2011.92

. Ester, A density-based algorithm for discovering clusters in large spatial databases with noise, KDD, pp.226-231, 1996.

. Faceli, Multi-objective clustering ensemble for gene expression data analysis, Neurocomputing, vol.72, issue.13-15, pp.7213-152763, 2009.
DOI : 10.1016/j.neucom.2008.09.025

G. Forestier, Connaissances et classification multistratégie d'objets complexes multisources, 2010.

D. Frey, B. J. Frey, and D. Dueck, Clustering by Passing Messages Between Data Points, Science, vol.315, issue.5814, p.315, 2007.
DOI : 10.1126/science.1136800

. Gan, Data clustering -theory, algorithms, and applications, 2007.
DOI : 10.1137/1.9780898718348

. Gan, 12. Grid-Based Clustering Algorithms, 2007.
DOI : 10.1137/1.9780898718348.ch12

URL : https://hal.archives-ouvertes.fr/hal-00179982

. Grozavu, . Bennani, N. Grozavu, and Y. Bennani, Topological collaborative clustering, Machine Learning Applications (Part I), 2010.

. Grozavu, Learning confidence exchange in Collaborative Clustering, The 2011 International Joint Conference on Neural Networks, pp.872-879, 2011.
DOI : 10.1109/IJCNN.2011.6033313

A. Guénoche, Consensus of partitions : a constructive approach, Advances in Data Analysis and Classification, vol.12, issue.2, pp.215-229, 2011.
DOI : 10.1007/s11634-011-0087-6

C. Heer, J. Heer, and E. H. Chi, Mining the Structure of User Activity using Cluster Stability, proceedings of the Web Analytics Workshop, SIAM Conference on Data Mining, 2002.

A. K. Jain, Data clustering: 50 years beyond K-means, Pattern Recognition Letters, vol.31, issue.8, pp.3-4, 2008.
DOI : 10.1016/j.patrec.2009.09.011

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.151.4286

. Kailing, Clustering multirepresented objects with noise, Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp.394-403, 2004.
DOI : 10.1007/978-3-540-24775-3_48

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.3.7441

K. Karypis, G. Karypis, and V. Kumar, A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs, SIAM Journal on Scientific Computing, vol.20, issue.1, pp.359-392, 1998.
DOI : 10.1137/S1064827595287997

R. Kaufman, L. Kaufman, and P. J. Rousseeuw, Finding Groups in Data. An Introduction to Cluster Analysis, 1990.

. Klein, From instance-level constraints to space-level constraints: Making the most of prior knowledge in data clustering, 2002.

H. Kriegel and A. Zimek, Subspace Clustering, Ensemble Clustering , Alternative Clustering, Multiview Clustering: What Can We Learn From Each Other, Proceedings of MultiClustKDD, 2010.

. Kulis, Semi-supervised graph clustering, Proceedings of the 22nd international conference on Machine learning , ICML '05, pp.457-464, 2005.
DOI : 10.1145/1102351.1102409

. Lashkari, D. Lashkari, and P. Golland, Convex clustering with exemplarbased models, Advances in Neural Information Processing Systems, pp.825-832, 2008.

T. Li, Clustering based on matrix approximation: a unifying view, Knowledge and Information Systems, vol.46, issue.5, pp.1-15, 2008.
DOI : 10.1007/s10115-007-0116-0

. Liu, BoostCluster, Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '07, pp.450-459, 2007.
DOI : 10.1145/1281192.1281242

U. Luxburg, A tutorial on spectral clustering, Statistics and Computing, vol.21, issue.1, pp.395-416, 2007.
DOI : 10.1007/s11222-007-9033-z

J. Macqueen, Some methods for classification and analysis of multivariate observations, Proceedings of the Fifth Berkeley Symposium on Mathematical statistics and probability, pp.281-297, 1967.

. Martin, Fusing Biomedical Multi-modal Data for Exploratory Data Analysis, ICANN 2006, Part II, pp.798-807, 2006.
DOI : 10.1007/11840930_83

. Mesghouni, Unsupervised horizontal collaboration based in SOM, International Journal of Computer Science and Information Technology, vol.3, issue.3, 2011.
DOI : 10.5121/ijcsit.2011.3320

. Ng, On spectral clustering: Analysis and an algorithm, In ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS, pp.849-856, 2001.

W. Pedrycz, Collaborative fuzzy clustering, Pattern Recognition Letters, vol.23, issue.14, pp.1675-1686, 2002.
DOI : 10.1016/S0167-8655(02)00130-7

S. Regnier, Sur quelques aspects mathématiques des problèmes de classification automatique, 1965.

G. Schwarz, Estimating the Dimension of a Model, The Annals of Statistics, vol.6, issue.2, pp.461-464, 1978.
DOI : 10.1214/aos/1176344136

. Shental, Computing gaussian mixture models with em using side-information, Advances in Neural Information Processing Systems 16, 2003.

M. Shi, J. Shi, and J. Malik, Normalized cuts and image segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000.

A. Strehl and J. Ghosh, Cluster ensembles ? a knowledge reuse framework for combining multiple partitions, J. Mach. Learn. Res, vol.3, pp.583-617, 2003.

. Sublemontier, Regroupement de données multi-représentées : une approche par k-moyenne flou, EGC 2009, 9è Journées Francophones Extraction et Gestion des Connaissances, Actes des ateliers, 2009.

. Sublemontier, Integrating Pairwise Constraints into Clustering Algorithms: Optimization-Based Approaches, 2011 IEEE 11th International Conference on Data Mining Workshops, 2011.
DOI : 10.1109/ICDMW.2011.103

URL : https://hal.archives-ouvertes.fr/hal-00632952

. Van-breukelen, Handwritten digit recognition by combined classifiers, pp.381-386, 1998.

R. Vega-pons, S. Vega-pons, and J. Ruiz-shulcloper, A SURVEY OF CLUSTERING ENSEMBLE ALGORITHMS, International Journal of Pattern Recognition and Artificial Intelligence, vol.25, issue.03, pp.337-372, 2011.
DOI : 10.1142/S0218001411008683

. Wagstaff, . Cardie, K. Wagstaff, and C. Cardie, Clustering with instance-level constraints, Proceedings of the Seventeenth International Conference on Machine Learning, pp.1103-1110, 2000.

. Wagstaff, Constrained k-means clustering with background knowledge, Proceedings of the Eighteenth International Conference on Machine Learning, ICML '01, pp.577-584, 2001.

. Wemmert, A COLLABORATIVE APPROACH TO COMBINE MULTIPLE LEARNING METHODS, International Journal on Artificial Intelligence Tools, vol.09, issue.01, pp.59-78, 2000.
DOI : 10.1142/S0218213000000069

B. Wiswedel, B. Wiswedel, and M. R. Berthold, Fuzzy Clustering in Parallel Universes, NAFIPS 2005, 2005 Annual Meeting of the North American Fuzzy Information Processing Society, pp.439-454, 2007.
DOI : 10.1109/NAFIPS.2005.1548598

. Xing, Distance metric learning, with application to clustering with side-information, Advances in Neural Information Processing Systems 15, pp.505-512, 2002.

. Xing, Distance metric learning with application to clustering with side-information, NIPS, pp.505-512, 2002.

. Yamanishi, Protein network inference from multiple genomic data: a supervised approach, Bioinformatics, vol.20, issue.Suppl 1, pp.363-370, 2004.
DOI : 10.1093/bioinformatics/bth910

URL : https://hal.archives-ouvertes.fr/hal-00433586

L. A. Zadeh, Fuzzy sets, Information and Control, vol.8, issue.3, pp.338-353, 1965.
DOI : 10.1016/S0019-9958(65)90241-X

. Zeng, Clustering genes using heterogeneous data sources, IJKDB, vol.1, issue.2, pp.12-28, 2010.

. Zhang, Parametric distance metric learning with label information, Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence, pp.1450-1452, 2003.