H. Akaike, Information theory and an extension of the maximum likelihood principle, Second International Symposium on Information Theory, pp.267-281, 1973.

M. Ankerst, M. M. Breunig, H. Peter-kriegel, and J. Sander, Optics: Ordering points to identify the clustering structure, ACM SIGMOD international conference on Management of data, pp.49-60, 1999.

J. Ardila, V. Tolpekin, and W. Bijker, Markov random field based super-resolution mapping for identification of urban trees in VHR images, IEEE International Geoscience & Remote Sensing Symposium, IGARSS 2010, pp.1402-1405, 2010.

P. Auer, N. Cesa-bianchi, and P. Fischer, Finite-time analysis of the multiarmed bandit problem, Machine Learning, vol.47, issue.2/3, pp.235-256, 2002.
DOI : 10.1023/A:1013689704352

M. Avriel, Nonlinear programming: analysis and methods. Prentice-Hall series in automatic computation, p.116, 1976.

J. Azimi and X. Fern, Adaptive cluster ensemble selection, pp.992-997, 2009.

P. Bachman, O. Alsharif, and D. Precup, Learning with pseudo-ensembles, Advances in Neural Information Processing Systems 27, pp.3365-3373, 2014.

N. M. Ball and R. J. Brunner, DATA MINING AND MACHINE LEARNING IN ASTRONOMY, International Journal of Modern Physics D, vol.19, issue.07, pp.1049-1106, 2010.
DOI : 10.1142/S0218271810017160

A. Bartkowiak, Intelligent Information Processing and Web Mining: Proceedings of the International IIS: IIPWM'04 Conference, p.121, 2004.

A. Bechtel, W. Puttmann, T. N. Carlson, and D. A. Ripley, On the Relation between NDVI, Fractional Vegetation Cover, and Leaf Area Index, Remote Sensing of Environment, vol.62, issue.3, pp.241-252, 1997.

S. Ben-david, U. Von-luxburg, and D. Pál, A Sober Look at Clustering Stability, Learning Theory, pp.5-19, 2006.
DOI : 10.1007/11776420_4

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.117.2171

P. Berkhin, A Survey of Clustering Data Mining Techniques, Tech. rep., Accrue Software, p.20, 2002.
DOI : 10.1007/3-540-28349-8_2

J. Besag, Statistical analysis of dirty pictures*, Journal of Applied Statistics, vol.6, issue.5-6, pp.259-302, 1986.
DOI : 10.1016/0031-3203(83)90012-2

J. C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms, p.137, 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.1-4, 2004.
DOI : 10.1109/ICDM.2004.10095

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

S. Bickel and T. Scheffer, Estimation of Mixture Models Using Co-EM, Proceedings of the ICML workshop on learning with multiple views, p.73, 2005.
DOI : 10.1007/11564096_9

C. Bishop, M. Svensén, and C. Williams, GTM: A principled alternative to the Self-Organizing Map, Artificial Neural Networks -ICANN 96, pp.165-170, 1996.
DOI : 10.1007/3-540-61510-5_31

C. M. Bishop, Neural Networks for Pattern Recognition, p.118, 1995.

C. M. Bishop, M. Svensén, I. Williams, and C. K. , GTM: The Generative Topographic Mapping, Neural Computation, vol.39, issue.1, pp.215-234, 1998.
DOI : 10.1007/BF01889678

T. Blaschke, Object based image analysis for remote sensing, ISPRS Journal of Photogrammetry and Remote Sensing, vol.65, issue.1, pp.2-16, 2010.
DOI : 10.1016/j.isprsjprs.2009.06.004

I. Bose and R. K. Mahapatra, Business data mining ??? a machine learning perspective, Information & Management, vol.39, issue.3, pp.211-225, 2001.
DOI : 10.1016/S0378-7206(01)00091-X

URL : http://ufdc.ufl.edu/LS00000942/00001

V. Boyarshinov, Machine learning in computational finance, p.136, 2005.

Y. Boykov, O. Veksler, and R. Zabih, Fast approximate energy minimization via graph cuts, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.23, issue.11, pp.1222-1239, 2001.
DOI : 10.1109/34.969114

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.112.6806

L. Breiman, Bagging predictors, Machine Learning, vol.10, issue.2, pp.123-140, 1996.
DOI : 10.1007/BF00058655

K. P. Burnham and D. R. Anderson, Multimodel Inference, Sociological Methods & Research, vol.27, issue.1, pp.261-304, 2004.
DOI : 10.1177/0049124104268644

N. V. Chawla, S. Eschrich, and L. O. Hall, Creating ensembles of classifiers, Proceedings 2001 IEEE International Conference on Data Mining, pp.580-581, 2001.
DOI : 10.1109/ICDM.2001.989568

Y. Chen, B. Qin, T. Liu, Y. Liu, and S. Li, The Comparison of SOM and K-means for Text Clustering, Computer and Information Science, vol.3, issue.2, pp.268-274, 2010.
DOI : 10.5539/cis.v3n2p268

S. Chu, J. F. Roddick, and J. Pan, Improved search strategies and extensions to k-medoids-based clustering algorithms, International Journal of Business Intelligence and Data Mining, vol.3, issue.2, pp.212-231, 2008.
DOI : 10.1504/IJBIDM.2008.020520

G. Cleuziou, M. Exbrayat, L. Martin, and J. Sublemontier, CoFKM: A Centralized Method for Multiple-View Clustering, 2009 Ninth IEEE International Conference on Data Mining, pp.6-9, 2009.
DOI : 10.1109/ICDM.2009.138

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

M. N. Condorcet, ´. E. Fromont, and R. Quiniou, Essai sur l'application de l'analysè a la probabilité des décisions renduesàrendues`renduesà la pluralité des voix. De l'imprimerie royale Learning rules from multisource data for cardiac monitoring, pp.3373-136, 2009.

A. Cornuéjols and C. Martin, Unsupervised Object Ranking Using Not Even Weak Experts, Lecture Notes in Computer Science, vol.4, issue.7062, pp.608-616, 2011.
DOI : 10.1007/978-3-540-30116-5_26

J. A. Cuesta-albertos and R. Fraiman, Impartial trimmed k-means for functional data, Computational Statistics & Data Analysis, vol.51, issue.10, pp.4864-4877, 2007.
DOI : 10.1016/j.csda.2006.07.011

URL : http://repositorio.udesa.edu.ar/jspui/bitstream/10908/495/1/%5bP%5d%5bW%5d%20doc32.pdf

S. Das and M. Mozer, A Unified Gradient-Descent/Clustering Architecture for Finite State Machine Induction, pp.19-26, 1993.

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. H. Day and H. Edelsbrunner, Efficient algorithms for agglomerative hierarchical clustering methods, Journal of Classification, vol.25, issue.1, pp.7-24, 1984.
DOI : 10.1007/BF01890115

A. P. Dempster, N. M. Laird, and D. B. Rubin, Maximum likelihood from incomplete data via the em algorithm, Journal of the Royal Statistical Society. Series B, vol.39, issue.137, pp.1-38, 1977.

B. Depaire, R. Falcon, K. Vanhoof, and G. Wets, PSO Driven Collaborative Clustering: a Clustering Algorithm for Ubiquitous Environments, Intelligent Data Analysis, vol.15, issue.61, pp.49-68, 2011.

J. C. Dunn, A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters, Journal of Cybernetics, vol.3, issue.3, pp.32-57, 1973.
DOI : 10.1080/01969727308546046

M. Ester, H. P. Kriegel, J. Sander, and X. Xu, A density-based algorithm for discovering clusters in large spatial databases with noise, International Conference on Knowledge Discovery and Information Retrieval, pp.226-231, 1996.

R. Falcon, G. Jeon, R. Bello, and J. Jeong, Learning Collaboration Links in a Collaborative Fuzzy Clustering Environment, Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence, MI- CAI'07, pp.483-495, 2007.
DOI : 10.1007/978-3-540-76631-5_46

M. A. Figueiredo and A. K. Jain, Unsupervised learning of finite mixture models, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, issue.3, pp.381-396, 2000.
DOI : 10.1109/34.990138

G. Forestier, C. Wemmert, and P. Gancarski, Collaborative multi-strategical classification for object-oriented image analysis, Workshop on Supervised and Unsupervised Ensemble Methods and Their Applications in conjunction with IbPRIA, pp.80-90, 2007.
DOI : 10.1007/978-3-540-78981-9_4

E. B. Fowlkes and C. L. Mallows, A Method for Comparing Two Hierarchical Clusterings, Journal of the American Statistical Association, vol.66, issue.383, pp.553-569, 1983.
DOI : 10.1080/01621459.1983.10478008

G. Frahling and C. Sohler, A fast k-means implementation using coresets, SCG '06: Proceedings of the twenty-second annual symposium on Computational geometry, pp.135-143, 2006.

A. Frank and A. Asuncion, UCI machine learning repository, p.155, 2010.

B. J. Frey and D. Dueck, Clustering by Passing Messages Between Data Points, Science, vol.315, issue.5814, p.27, 2007.
DOI : 10.1126/science.1136800

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.121.3145

J. H. Friedman and J. J. Meulman, Clustering objects on subsets of attributes (with discussion), Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.23, issue.4, pp.815-849, 2004.
DOI : 10.1093/bioinformatics/18.3.413

M. Ghassany, Contributions to collaborative clustering, pp.132-133, 2012.

M. Ghassany, N. Grozavu, and Y. Bennani, COLLABORATIVE CLUSTERING USING PROTOTYPE-BASED TECHNIQUES, International Journal of Computational Intelligence and Applications, vol.11, issue.03, pp.71-124, 2012.
DOI : 10.1142/S1469026812500174

R. C. Gonzalez and R. E. Woods, Digital Image Processing, p.139, 2006.

P. J. Green, On Use of the EM Algorithm for Penalized Likelihood Estimation, Journal of the Royal Statistical Society. Series BMethodological), vol.52, issue.3, pp.443-452, 1990.

N. Grozavu, Collaborative unsupervised learning and cluster characterization, p.143, 2009.

N. Grozavu and Y. Bennani, Topological collaborative clustering, Australian Journal of Intelligent Information Processing Systems, vol.12, issue.143, pp.96-132, 2010.

N. Grozavu, G. Cabanes, and Y. Bennani, Diversity analysis in collaborative clustering, 2014 International Joint Conference on Neural Networks (IJCNN), pp.1754-1761, 2014.
DOI : 10.1109/IJCNN.2014.6889528

N. Grozavu and B. Y. , Topological collaborative clustering, LNCS Springer of ICONIP'10 : 17th International Conference on Neural Information Processing, p.143, 2010.

S. Guha, R. Rastogi, and K. Shim, CURE, ACM SIGMOD Record, vol.27, issue.2, pp.73-84, 1998.
DOI : 10.1145/276305.276312

M. Halkidi, Y. Batistakis, and M. Vazirgiannis, Clustering validity checking methods, ACM SIGMOD Record, vol.31, issue.3, p.31, 2001.
DOI : 10.1145/601858.601862

H. Hamdan and C. Hajjar, Kohonen neural networks for interval-valued data clustering, International Journal of Advanced Computer Science, vol.2, issue.10, pp.412-419, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00862567

F. Hamdi and Y. Bennani, Learning random subspace novelty detection filters, The 2011 International Joint Conference on Neural Networks, pp.2273-2280, 2011.
DOI : 10.1109/IJCNN.2011.6033512

J. Han, M. Kamber, and J. Pei, Data Mining, 2011.
DOI : 10.1007/978-1-4899-7993-3_104-2

M. H. Hansen and B. Yu, Model Selection and the Principle of Minimum Description Length, Journal of the American Statistical Association, vol.96, issue.454, pp.746-774, 1998.
DOI : 10.1198/016214501753168398

J. Hartigan and M. Wong, Algorithm AS 136: A K-Means Clustering Algorithm, Applied Statistics, vol.28, issue.1, pp.100-108, 1979.
DOI : 10.2307/2346830

C. A. Hernández-gracidas and L. E. Sucar, Markov Random Fields and Spatial Information to Improve Automatic Image Annotation, Lecture Notes in Computer Science, vol.4872, pp.879-892, 2007.
DOI : 10.1007/978-3-540-77129-6_74

V. Hodge and J. Austin, A Survey of Outlier Detection Methodologies, Artificial Intelligence Review, vol.22, issue.2, pp.85-126, 2004.
DOI : 10.1023/B:AIRE.0000045502.10941.a9

T. Hu, Y. Yu, J. Xiong, and S. Y. Sung, Maximum likelihood combination of multiple clusterings, Pattern Recognition Letters, vol.27, issue.13, pp.1457-1464, 2006.
DOI : 10.1016/j.patrec.2006.02.013

D. Huang, J. Lai, and C. Wang, Combining multiple clusterings via crowd agreement estimation and multi-granularity link analysis, Neurocomputing, vol.170, pp.240-250, 2015.
DOI : 10.1016/j.neucom.2014.05.094

URL : http://arxiv.org/abs/1405.1297

L. Hubert and P. Arabie, Comparing partitions, Journal of Classification, vol.78, issue.1, pp.193-218, 1985.
DOI : 10.1007/BF01908075

N. Iam-on and T. Boongoen, Comparative study of matrix refinement approaches for ensemble clustering, Machine Learning, vol.23, issue.21, pp.269-300, 2015.
DOI : 10.1007/s10994-013-5342-y

A. K. Jain and R. C. Dubes, Algorithms for clustering data, p.36, 1988.

A. K. Jain, M. N. Murty, and P. J. Flynn, Data clustering: a review, ACM Computing Surveys, vol.31, issue.3, pp.264-323, 1999.
DOI : 10.1145/331499.331504

B. Jasani, M. Pesaresi, S. Schneiderbauer, and G. Zeug, Remote Sensing from Space: Supporting International Peace and Security, p.40, 2009.
DOI : 10.1007/978-1-4020-8484-3

R. Jenssen, D. Erdogmus, K. E. Ii, J. C. Principe, and T. Eltoft, Information cut for clustering using a gradient descent approach, Pattern Recognition, vol.40, issue.3, pp.796-806, 2007.
DOI : 10.1016/j.patcog.2006.06.028

B. Jähne, Digital Image Processing 6th Edition, p.40, 2005.

K. Jong, J. Mary, A. Cornuéjols, E. Marchiori, and M. Sebag, Ensemble Feature Ranking, Lecture Notes in Computer Science, vol.3202, pp.267-278, 2004.
DOI : 10.1007/978-3-540-30116-5_26

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.63.5577

T. Kanungo, D. M. Mount, N. S. Netanyahu, C. D. Piatko, R. Silverman et al., An efficient k-means clustering algorithm: analysis and implementation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, issue.7, pp.881-892, 2002.
DOI : 10.1109/TPAMI.2002.1017616

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.43.2981

Z. Kato, M. Berthod, and J. Zerubia, A hierarchical Markov random field model and multi-temperature annealing for parallel image classification, p.139, 1993.
URL : https://hal.archives-ouvertes.fr/inria-00074736

R. Kindermann and J. L. Snell, Markov Random Fields and Their Applications, AMS, vol.1, p.139, 1980.
DOI : 10.1090/conm/001

J. Kittler and J. Föglein, Contextual classification of multispectral pixel data, Image and Vision Computing, vol.2, issue.1, pp.13-29, 1984.
DOI : 10.1016/0262-8856(84)90040-4

J. Kittler, M. Hatef, R. P. Duin, and J. Matas, On combining classifiers, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.20, issue.3, pp.226-239, 1998.
DOI : 10.1109/34.667881

T. Kohonen, Self-Organization and Associative Memory, p.114, 1984.
DOI : 10.1007/978-3-642-88163-3

T. Kohonen, Self-organizing Maps, p.117, 1995.

T. Kohonen, Self-organizing Maps, pp.121-146, 2001.

I. Kononenko, Machine learning for medical diagnosis: history, state of the art and perspective, Artificial Intelligence in Medicine, vol.23, issue.1, pp.89-109, 2001.
DOI : 10.1016/S0933-3657(01)00077-X

M. A. Krieger and E. P. Green, A Generalized Rand-Index Method for Consensus Clustering of Separate Partitions of the Same Data Base, Journal of Classification, vol.16, issue.1, pp.63-89, 1999.
DOI : 10.1007/s003579900043

H. W. Kuhn and A. W. Tucker, Nonlinear programming, Proceedings of 2nd Berkeley Symposium, pp.481-492, 1951.

V. Kuleshov and D. Precup, Algorithms for multi-armed bandit problems, pp.6028-133, 2014.

L. I. Kuncheva, Classifier ensembles: Facts, fiction, faults and future (2008). (slides, plenary talk, p.60

M. Leordeanu, M. Hebert, and R. Sukthankar, An integer projected fixed point method for graph matching and map inference, pp.1114-1122, 2009.

C. X. Li and J. Yu, A Novel Fuzzy C-Means Clustering Algorithm, In: RSKT, Lecture Notes in Computer Science, vol.4062, pp.510-515, 2006.
DOI : 10.1007/11795131_74

A. Lourenço, S. Rotabuì-o, N. Rebagliati, A. Fred, M. Figueiredo et al., Probabilistic consensus clustering using evidence accumulation, Machine Learning, vol.4, issue.1, pp.331-357, 2015.
DOI : 10.1007/s10994-013-5339-6

U. Von-luxburg, Clustering stability: An overview, Foundations and Trends in Machine Learning, vol.2, issue.97, pp.235-274, 2010.

D. Ma and A. Zhang, An Adaptive Density-Based Clustering Algorithm for Spatial Database with Noise. Data Mining, pp.467-470, 2004.

J. B. Macqueen, Some methods for classification and analysis of multivariate observations, Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, pp.281-297, 1967.

M. Meila and J. Shi, Learning segmentation by random walks, Advances in Neural Information Processing Systems, pp.873-879, 2001.

M. K. Pakhira, S. Bandyopadhyay, and U. Maulik, Validity index for crisp and fuzzy clusters, Pattern Recognition, vol.37, issue.3, pp.487-501, 2004.
DOI : 10.1016/j.patcog.2003.06.005

N. R. Pal and S. K. Pal, A review on image segmentation techniques, Pattern Recognition, vol.26, issue.9, pp.1277-1294, 1993.
DOI : 10.1016/0031-3203(93)90135-J

T. Pavlidis, Structural Pattern Recognition, p.139, 1977.
DOI : 10.1007/978-3-642-88304-0

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

W. Pedrycz, Fuzzy clustering with a knowledge-based guidance, Pattern Recognition Letters, vol.25, issue.4, pp.469-480, 2004.
DOI : 10.1016/j.patrec.2003.12.010

W. Pedrycz, Interpretation of clusters in the framework of shadowed sets, Pattern Recognition Letters, vol.26, issue.15, pp.2439-2449, 2005.
DOI : 10.1016/j.patrec.2005.05.001

W. Pedrycz, Knowledge-Based Clustering, p.143, 2005.
DOI : 10.1002/0471708607

W. Pedrycz and K. Hirota, A consensus-driven fuzzy clustering, Pattern Recognition Letters, vol.29, issue.9, pp.1333-1343, 2008.
DOI : 10.1016/j.patrec.2008.02.015

D. Pelleg and A. Moore, X-means: Extending K-means with efficient estimation of the number of clusters, Proceedings of the 17th International Conf. on Machine Learning, pp.727-734, 2000.

A. Puissant, Information géographique et imagè a très haute résolution, p.43, 2006.

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

J. A. Richards and X. Jia, Remote Sensing Digital Image Analysis: An Introduction, p.40, 1999.

S. Roth and M. J. Black, Fields of Experts, Markov Random Fields for Vision and Image Processing, pp.297-310, 2011.
DOI : 10.1007/s11263-008-0197-6

S. Rougier and A. Puissant, Improvements of urban vegetation segmentation and classification using multi-temporal pleiades images, 5th International Conference on Geographic Object-Based Image Analysis p, pp.6-140, 2014.

P. J. Rousseeuw and A. M. Leroy, Robust Regression and Outlier Detection, p.33, 1987.
DOI : 10.1002/0471725382

R. Rousseeuw, Silhouettes: A graphical aid to the interpretation and validation of cluster analysis, Journal of Computational and Applied Mathematics, vol.20, issue.86, pp.53-65, 1987.
DOI : 10.1016/0377-0427(87)90125-7

S. Ryali, T. Chen, K. Supekar, and V. Menon, A parcellation scheme based on von Mises-Fisher distributions and Markov random fields for segmenting brain regions using resting-state fMRI, NeuroImage, vol.65, pp.83-96, 2013.
DOI : 10.1016/j.neuroimage.2012.09.067

J. Sander, M. Ester, H. P. Kriegel, and X. Xu, Density-based clustering in spatial databases: The algorithm gdbscan and its applications, Data Mining and Knowledge Discovery, vol.2, issue.2, pp.169-194, 1998.
DOI : 10.1023/A:1009745219419

J. Sander, M. Ester, H. P. Kriegel, and X. Xu, Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications, Data Mining and Knowledge Discovery, vol.2, issue.2, pp.169-194, 1998.
DOI : 10.1023/A:1009745219419

R. E. Schapire, The strength of weak learnability, Mach. Learn, vol.5, issue.61, pp.197-227, 1990.

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

S. Shekhar, S. Member, P. R. Schrater, R. R. Vatsavai, W. Wu et al., Spatial contextual classification and prediction models for mining geospatial data, IEEE Transactions on Multimedia, vol.4, issue.2, pp.174-188, 2002.
DOI : 10.1109/TMM.2002.1017732

J. Shi and J. Malik, Normalized cuts and image segmentation, IEEE Trans. Pattern Anal. Mach. Intell, vol.22, issue.28, pp.888-905, 2000.

A. D. Silva, Y. Lechevallier, A. T. De, F. De-carvalho, and B. Trousse, Mining Web Usage Data for Discovering Navigation Clusters, 11th IEEE Symposium on Computers and Communications (ISCC'06), pp.910-915, 2006.
DOI : 10.1109/ISCC.2006.102

H. Steinhaus, Sur la division des corps matériels en parties, Bull. Acad. Polon. Sci. Cl. III, vol.4, pp.801-804, 1956.

A. Strehl, J. Ghosh, and C. Cardie, Cluster ensembles -a knowledge reuse framework for combining multiple partitions, Journal of Machine Learning Research, vol.3, issue.65, pp.583-617, 2002.

J. Sublemontier, Unsupervised collaborative boosting of clustering: An unifying framework for multi-view clustering, multiple consensus clusterings and alternative clustering, The 2013 International Joint Conference on Neural Networks (IJCNN), pp.1-8, 2013.
DOI : 10.1109/IJCNN.2013.6706911

URL : https://hal.archives-ouvertes.fr/hal-00813201

J. Sublime, Y. Bennani, and A. Cornuéjols, A compactness-based icm algorithm for vhr satellite image segmentation, Extraction et Gestion des Connaissances EGC2015, p.139, 2015.

J. Sublime, A. Cornuéjols, and Y. Bennani, A New Energy Model for the Hidden Markov Random Fields, ICONIP 2014, Part II, pp.60-67, 2014.
DOI : 10.1007/978-3-319-12640-1_8

J. Sublime, A. Cornuéjols, and Y. Bennani, Collaborative-Based Multi-scale Clustering in Very High Resolution Satellite Images, The 23rd International Conference on Neural Information Processing, p.144, 2016.
DOI : 10.1007/978-3-319-46675-0_17

J. Sublime, N. Grozavu, Y. Bennani, and A. Cornuéjols, Collaborative clustering with heterogeneous algorithms, 2015 International Joint Conference on Neural Networks (IJCNN), p.145, 2015.
DOI : 10.1109/IJCNN.2015.7280351

J. Sublime, N. Grozavu, Y. Bennani, and A. Cornuéjols, Vertical collaborative clustering using generative topographic maps, 2015 7th International Conference of Soft Computing and Pattern Recognition (SoCPaR), p.146, 2015.
DOI : 10.1109/SOCPAR.2015.7492807

URL : https://hal.archives-ouvertes.fr/hal-01461467

J. Sublime, N. Grozavu, G. Cabanes, Y. Bennani, and A. Cornuéjols, From horizontal to vertical collaborative clustering using generative topographic maps, International Journal of Hybrid Intelligent Systems, vol.12, issue.4, p.146, 2016.
DOI : 10.3233/HIS-160219

URL : https://hal.archives-ouvertes.fr/hal-01461467

J. Sublime, A. Troya-galvis, Y. Bennani, P. Gancarski, and A. Cornuéjols, Semantic rich ICM algorithm for VHR satellite images segmentation, 2015 14th IAPR International Conference on Machine Vision Applications (MVA), p.140, 2015.
DOI : 10.1109/MVA.2015.7153129

P. N. Tan, M. Steinbach, and V. Kumar, Introduction to Data Mining, p.31, 2005.

K. Tanabe, B. Lu?i´lu?i´c, D. Ami´cami´c, T. Kurita, M. Kaihara et al., Prediction of carcinogenicity for diverse chemicals based on substructure grouping and SVM modeling, Molecular Diversity, vol.27, issue.4, pp.789-802, 2010.
DOI : 10.1007/s11030-010-9232-y

Y. Tang, X. Wu, and W. Bu, Saliency Detection Based on Graph-Structural Agglomerative Clustering, Proceedings of the 23rd ACM international conference on Multimedia, MM '15, pp.1083-1086, 2015.
DOI : 10.1145/2733373.2806287

A. L. Tarca, V. J. Carey, X. W. Chen, R. Romero, and S. Dr?-aghici, Machine Learning and Its Applications to Biology, PLoS Computational Biology, vol.365, issue.6, pp.116-136, 2007.
DOI : 0140-6736(2005)365[0488:POCOWM]2.0.CO;2

C. Y. Tsai and C. C. Chiu, Developing a feature weight self-adjustment mechanism for a K-means clustering algorithm, Computational Statistics & Data Analysis, vol.52, issue.10, pp.4658-4672, 2008.
DOI : 10.1016/j.csda.2008.03.002

R. Unnikrishnan and M. Hebert, Measures of Similarity, 2005 Seventh IEEE Workshops on Applications of Computer Vision (WACV/MOTION'05), Volume 1, pp.394-152, 2005.
DOI : 10.1109/ACVMOT.2005.71

R. Unnikrishnan, C. Pantofaru, and M. Hebert, A Measure for Objective Evaluation of Image Segmentation Algorithms, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), Workshops, pp.34-54, 2005.
DOI : 10.1109/CVPR.2005.390

T. Vatanen, M. Osmala, T. Raiko, K. Lagus, M. Sysi-aho et al., Self-organization and missing values in SOM and GTM, Neurocomputing, vol.147, pp.60-70, 2015.
DOI : 10.1016/j.neucom.2014.02.061

J. Vesanto and E. Alhoniemi, Clustering of the self-organizing map, IEEE Transactions on Neural Networks, vol.11, issue.3, pp.586-600, 2000.
DOI : 10.1109/72.846731

P. Viswanath and R. Pinkesh, l-DBSCAN : A Fast Hybrid Density Based Clustering Method, 18th International Conference on Pattern Recognition (ICPR'06), pp.912-915, 2006.
DOI : 10.1109/ICPR.2006.741

S. I. Vrieze, Model selection and psychological theory: A discussion of the differences between the Akaike information criterion (AIC) and the Bayesian information criterion (BIC)., Psychological Methods, vol.17, issue.2, pp.228-243, 2012.
DOI : 10.1037/a0027127

X. N. Wang, J. M. Wei, H. Jin, G. Yu, and H. W. Zhang, Probabilistic Confusion Entropy for Evaluating Classifiers, Entropy, vol.15, issue.11, pp.4969-4992, 2013.
DOI : 10.3390/e15114969

URL : http://doi.org/10.3390/e15114969

J. H. Ward, Hierarchical Grouping to Optimize an Objective Function, Journal of the American Statistical Association, vol.58, issue.301, pp.236-244, 1963.
DOI : 10.1007/BF02289263

J. M. Wei, X. J. Yuan, Q. H. Hu, and S. Q. Wang, A novel measure for evaluating classifiers, Expert Systems with Applications, vol.37, issue.5, pp.3799-3809, 2010.
DOI : 10.1016/j.eswa.2009.11.040

J. Weinman, A brief introduction to ICM, p.46, 2008.

C. Wemmert, Classification hybride distribuée par collaboration de méthodes non supervisées, pp.72-132, 2000.

C. Wemmert and P. Gancarski, A multi-view voting method to combine unsupervised classifications, Artificial Intelligence and Applications, pp.447-452, 2002.

Q. Weng, Remote sensing of impervious surfaces in the urban areas: Requirements, methods, and trends, Remote Sensing of Environment, vol.117, issue.0, pp.34-49, 2012.
DOI : 10.1016/j.rse.2011.02.030

D. H. Wolpert, Stacked generalization, Neural Networks, vol.5, issue.2, pp.241-259, 1992.
DOI : 10.1016/S0893-6080(05)80023-1

C. F. Wu, On the Convergence Properties of the EM Algorithm, The Annals of Statistics, vol.11, issue.1, pp.95-103, 1983.
DOI : 10.1214/aos/1176346060

R. Xu, I. Wunsch, and D. , Survey of Clustering Algorithms, IEEE Transactions on Neural Networks, vol.16, issue.3, pp.645-678, 2005.
DOI : 10.1109/TNN.2005.845141

Q. Yu, A. Sorjamaa, Y. Miche, and E. Severin, A methodology for time series prediction in finance, ESTSP, European Symposium on Time Series Prediction, pp.285-293, 2008.

M. Zarinbal, M. F. Zarandi, and I. Turksen, Relative entropy collaborative fuzzy clustering method, Pattern Recognition, vol.48, issue.3, pp.933-940, 2015.
DOI : 10.1016/j.patcog.2014.09.018

D. Zhang, Y. Wang, L. Zhou, H. Yuan, and D. Shen, Multimodal classification of Alzheimer's disease and mild cognitive impairment, NeuroImage, vol.55, issue.3, pp.856-867, 2011.
DOI : 10.1016/j.neuroimage.2011.01.008

L. Zhang and Q. Ji, Image Segmentation with a Unified Graphical Model, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.32, issue.8, pp.1406-1425, 2010.
DOI : 10.1109/TPAMI.2009.145

S. Zhang, C. Zhang, and X. Wu, Knowledge Discovery in Multiple Databases. Advanced Information and Knowledge Processing, p.143, 2004.

W. Zhang, L. Zhang, Y. Hu, R. Jin, D. Cai et al., Sparse Learning with Stochastic Composite Optimization, Proceedings of the 28th AAAI Conference on Artificial Intelligence (AAAI), pp.893-899, 2014.
DOI : 10.1109/TPAMI.2016.2578323

W. Zhang, D. Zhao, and X. Wang, Agglomerative clustering via maximum incremental path integral, Pattern Recognition, vol.46, issue.11, pp.3056-3065, 2013.
DOI : 10.1016/j.patcog.2013.04.013

Y. Zhang, M. Brady, and S. M. Smith, Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm, IEEE Transactions on Medical Imaging, vol.20, issue.1, pp.45-57, 2001.
DOI : 10.1109/42.906424

A. Zimek and J. Vreeken, The blind men and the elephant: on meeting the problem of multiple truths in data from clustering and pattern mining perspectives, Machine Learning, vol.5, issue.5, pp.121-155, 2015.
DOI : 10.1007/s10994-013-5334-y

S. W. Zucker, Region growing: Childhood and adolescence, Computer Graphics and Image Processing, vol.5, issue.3, pp.382-399, 1976.
DOI : 10.1016/S0146-664X(76)80014-7