A. Anagnostopoulos, A. Dasgupta, and R. Kumar, Approximation algorithms for coclustering, Proceedings of the twenty-seventh ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems, PODS '08, pp.201-210, 2008.

A. Banerjee, I. Dhillon, J. Ghosh, S. Merugu, and D. S. Modha, A generalized maximum entropy approach to bregman co-clustering and matrix approximation, Proceedings of the 2004 ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '04, pp.1919-1986, 2007.
DOI : 10.1145/1014052.1014111

R. Bekkerman and J. Jeon, com DOI : 10.1007/s10618-012-0248-z 3 Multi-modal clustering for multimedia collections, Computer Vision and Pattern Recognition, IEEE Computer Society Conference on Vision and Pattern Recognition, pp.1-8, 2007.

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

D. Chakrabarti, S. Papadimitriou, D. S. Modha, and C. Faloutsos, Fully automatic crossassociations, pp.79-88, 2004.

Y. Chen, L. Wang, and M. Dong, Semi-supervised Document Clustering with Simultaneous Text Representation and Categorization, pp.211-226, 2009.
DOI : 10.1007/3-540-45372-5_46

Y. Chen, L. Wang, and M. Dong, Non-Negative Matrix Factorization for Semisupervised Heterogeneous Data Coclustering, IEEE Transactions on Knowledge and Data Engineering, vol.22, issue.10, pp.1459-1474, 2010.
DOI : 10.1109/TKDE.2009.169

H. Cho, I. S. Dhillon, Y. Guan, and S. Sra, Minimum Sum-Squared Residue Co-clustering of Gene Expression Data, Proceedings of SIAM SDM, p.2004, 2004.
DOI : 10.1137/1.9781611972740.11

G. Cleuziou, M. Exbrayat, L. Martin, and J. H. Sublemontier, 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

J. Demsar, Statistical comparisons of classifiers over multiple data sets, Journal of Machine Learning Research, vol.7, pp.1-30, 2006.

I. S. Dhillon, S. Mallela, and D. S. Modha, Information-theoretic co-clustering, Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '03, pp.89-98, 2003.
DOI : 10.1145/956750.956764

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.13.9802

G. Forman, An extensive empirical study of feature selection metrics for text classification, J. Mach. Learn. Res, vol.3, pp.1289-1305, 2003.

B. Gao, T. Y. Liu, and W. Y. Ma, Star-structured high-order heterogeneous data coclustering based on consistent information theory, ICDM '06: Proceedings of the Sixth International Conference on Data Mining, pp.880-884, 2006.

L. A. Goodman and W. H. Kruskal, Measures of association for cross classification, Journal of the American Statistical Association, vol.49, pp.732-764, 1954.

G. Greco, A. Guzzo, and L. Pontieri, Co-clustering multiple heterogeneous domains: Linear combinations and agreements, IEEE Transactions on Knowledge and Data Engineering, vol.99, 2009.
DOI : 10.1109/tkde.2009.207

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

D. Ienco, R. G. Pensa, and R. Meo, Parameter-Free Hierarchical Co-clustering by n-Ary Splits, Proceedings of ECML/PKDD 2009, pp.580-595, 2009.
DOI : 10.1007/978-3-540-76993-4_57

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.624.6919

A. Jaszkiewicz, Genetic local search for multi-objective combinatorial optimization, European Journal of Operational Research, vol.137, issue.1, pp.50-71, 2002.
DOI : 10.1016/S0377-2217(01)00104-7

E. Keogh, S. Lonardi, and C. A. Ratanamahatana, Towards parameter-free data mining, Proceedings of the 2004 ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '04, pp.206-215, 2004.
DOI : 10.1145/1014052.1014077

J. Knowles, L. Thiele, and E. Zitzler, A Tutorial on the Performance Assessment of Stochastic Multiobjective Optimizers, TIK Report, vol.214, 2006.

D. D. Lee and D. D. Seung, Algorithms for non-negative matrix factorization, Advances in Neural Information Processing Systems, pp.556-562, 2001.

A. Liefooghe, J. Humeau, S. Mesmoudi, L. Jourdan, and E. G. Talbi, On dominancebased multiobjective local search: design, implementation and experimental analysis on scheduling and traveling salesman problems, Journal of Heuristics, pp.1-36, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00628215

B. Long, Z. M. Zhang, X. Wú, and P. S. Yu, Spectral clustering for multi-type relational data, Proceedings of the 23rd international conference on Machine learning , ICML '06, pp.585-592, 2006.
DOI : 10.1145/1143844.1143918

B. Long, Z. M. Zhang, and P. S. Yu, A probabilistic framework for relational clustering, Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '07, pp.470-479, 2007.
DOI : 10.1145/1281192.1281244

L. Paquete, Stochastic Local Search Algorithms for Multiobjective Combinatorial Optimization, 2006.
DOI : 10.1201/9781420010749.ch29

L. Paquete and T. Stützle, A study of stochastic local search algorithms for the biobjective QAP with correlated flow matrices, European Journal of Operational Research, vol.169, issue.3, pp.943-959, 2006.
DOI : 10.1016/j.ejor.2004.08.024

R. G. Pensa and J. F. Boulicaut, Constrained Co-clustering of Gene Expression Data, Proceedings of SIAM SDM, pp.25-36, 2008.
DOI : 10.1137/1.9781611972788.3

URL : https://hal.archives-ouvertes.fr/hal-01500611

D. Ramage, P. Heymann, C. D. Manning, and H. Garcia-molina, Clustering the tagged web, Proceedings of the Second ACM International Conference on Web Search and Data Mining, WSDM '09, pp.54-63, 2009.
DOI : 10.1145/1498759.1498809

C. Robardet and F. Feschet, Comparison of Three Objective Functions for Conceptual Clustering, Proceedings PKDD'01, LNAI, pp.399-410, 2001.
DOI : 10.1007/3-540-44794-6_33

C. Robardet and F. Feschet, Efficient local search in conceptual clustering, Proceedings DS'01, pp.323-335, 2001.

D. J. Sheskin, Handbook of Parametric and Nonparametric Statistical Procedures, 4 edn, CRC, 2007.

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