B. Appendix and . Bouchareb, AKDM-8). Springer. (C p 16 ) or as Exec-managerial, Prof-specialty, Protective-serv, ArmedForces or Tech-support (C p 7 ), work more than 40 hours a week, Advances in Knowledge Discovery and Management, vol.8, 2017.

, have more than three years and less than ten years of education (C p 9 and C p 13 ), males (C p 15 ), are likely to work in Farming-fishing, Handlers-cleaners, Transport-moving, Machineop-inspct, Craft-repair (C p 16 ) or as Exec-managerial, Prof-specialty, Protective-serv, Armed-Forces and Tech-support (C p 7 ), more than 40 hours a week, cluster of instances C u 12 containing 3430 individuals who are probably married (C p 3 )

R. Bhatia, blockcluster: An r package for model based co-clustering, 2014.
DOI : 10.18637/jss.v076.i09
URL : https://hal.archives-ouvertes.fr/hal-01093554

. Bock and H. Bock, Simultaneous clustering of objects and variables, Analyse des données et Informatique, pp.187-203, 1979.

L. Brault, V. Brault, and A. Lomet, Revue des méthodes pour la classification jointe des lignes et des colonnes d'un tableau, Journal de la Société Française de Statistique, vol.156, issue.3, pp.27-51, 2015.

C. Cheng, Y. Cheng, and G. M. Church, Biclustering of expression data, Proceedings of the International Conference on Intelligent Systems for Molecular Biology, vol.8, pp.93-103, 2000.

. Dhillon, Informationtheoretic co-clustering, Proceedings of the ninth international conference on Knowledge discovery and data mining, pp.89-98, 2003.

I. J. Good-;-good, G. Govaert, and M. Nadif, Her Majesty's Stationery Office, London. Govaert and Nadif, In Mathematics and Computer Science in Biology and Medicine, vol.36, issue.2, pp.463-473, 1965.

N. Govaert, G. Govaert, and M. Nadif, Block clustering with Bernoulli mixture models : Comparison of different approaches, Computational Statistics and Data Analysis, vol.52, issue.6, pp.3233-3245, 2008.
DOI : 10.1016/j.csda.2007.09.007

N. Govaert, G. Govaert, and M. Nadif, Co-Clustering, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00933301

. Hartigan and J. A. Hartigan, Clustering Algorithms, 1975.

N. ;. Hintze, J. L. Hintze, and R. D. Nelson, Violin plots: A box plot-density trace synergism, The American Statistician, vol.52, issue.2, pp.181-184, 1998.

A. Hubert, L. Hubert, and P. Arabie, Comparing partitions. Journal of Classification, vol.2, pp.193-218, 1985.

M. Mariadassou, M. Mariadassou, and C. Matias, Convergence of the groups posterior distribution in latent or stochastic block models, Bernoulli, vol.21, issue.1, pp.537-573, 2015.
URL : https://hal.archives-ouvertes.fr/hal-00713120

G. Mcparland, D. Mcparland, and I. C. Gormley, Model based clustering for mixed data: clustmd. Advances in Data Analysis and Classification, vol.10, pp.155-169, 2016.

H. Bibliography-akaike, A new look at the statistical model identification, IEEE Transactions on Automatic Control, vol.19, issue.6, p.27, 1974.

R. S. Amant and P. R. Cohen, Issues in automating exploratory data analysis, 1995.

A. Anagnostopoulos, A. Dasgupta, and R. Kumar, Approximation algorithms for co-clustering, Proceedings of the Twenty-seventh ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, PODS '08, pp.201-210, 2008.

M. R. Anderberg, Cluster Analysis for Applications, vol.11, p.9, 1973.

A. Banerjee, I. Dhillon, J. Ghosh, S. Merugu, and D. S. Modha, A generalized maximum entropy approach to bregman co-clustering and matrix approximation, J. Mach. Learn. Res, vol.8, p.26, 2007.

E. Beh and R. Lombardo, Correspondence Analysis: Theory, Practice and New Strategies. Wiley, 2014.

C. Biernacki, G. Celeux, G. , and G. , Assessing a mixture model for clustering with the integrated completed likelihood, IEEE Trans. Pattern Anal. Mach. Intell, vol.22, issue.7, p.27, 2000.

C. M. Bishop, Pattern Recognition and Machine Learning (Information Science and Statistics), 2006.

A. Bouchareb, M. Boullé, F. Clérot, and F. Rossi, Application du coclustering à l'analyse exploratoire d'une table de données, p.17, 2017.

, Journées Francophones Extraction et Gestion des Connaissances, vol.33, pp.177-188, 2017.

A. Bouchareb, M. Boullé, F. Clérot, and F. Rossi, Co-clustering based exploratory analysis of mixed-type data tables, Advances in Knowledge Discovery and Management, vol.8, 2018.

. Springer,

A. Bouchareb, M. Boullé, F. Clérot, and F. Rossi, Model based coclustering of mixed numerical and binary data, Advances in Knowledge Discovery and Management, vol.8, 2018.

A. Bouchareb, M. Boullé, and F. Rossi, Co-clustering de données mixtes à base des modèles de mélange, 17ème Journées Francophones Extraction et Gestion des Connaissances, pp.141-152, 2017.

A. Bouchareb, M. Boullé, F. Rossi, C. , and F. , Un modèle bayésien de co-clustering de données mixtes, Extraction et Gestion des Connaissances, EGC 2018, pp.275-280, 2018.

M. Boullé, Recherche d'une représentation des données efficace pour la fouille de grandes bases de données, Ecole Nationale Supérieure des Télécommunications, 2007.

M. ;. Boullé, G. Cawley, G. Dror, and A. Saffari, Data grid models for preparation and modeling in supervised learning, Hands-On Pattern Recognition: Challenges in Machine Learning, vol.37, p.64, 2011.

V. Brault, Estimation et sélection de modèle pour le modèle des blocs latents, 2014.

V. Brault and A. Lomet, Revue des méthodes pour la classification jointe des lignes et des colonnes d'un tableau, Journal de la Société Française de Statistique, vol.156, issue.3, p.18, 2015.

V. Brault and M. Mariadassou, Co-clustering through latent bloc model : a review, Special Issue on Networks and Statistics, p.156, 2015.
URL : https://hal.archives-ouvertes.fr/hal-02088216

N. D. Buono and G. Pio, Non-negative matrix tri-factorization for co-clustering: An analysis of the block matrix, Information Sciences, vol.301, pp.13-26, 2015.

C. Cano, L. Adarve, J. López, and A. Blanco, Possibilistic approach for biclustering microarray data, Computers in Biology and Medicine, vol.37, issue.10, pp.1426-1436, 2007.

J. Catlett, On changing continuous attributes into ordered discrete attributes, 1991.

M. Charrad and M. B. Ahmed, Simultaneous clustering: A survey, 4th International Conference of PReMI, p.18, 2011.
URL : https://hal.archives-ouvertes.fr/hal-01125890

Y. Chen, T. A. Wheeler, and M. J. Kochenderfer, Learning discrete bayesian networks from continuous data, J. Artif. Int. Res, vol.59, issue.1, pp.103-132, 2017.

Y. Cheng and G. M. Church, Biclustering of expression data, Proceedings of the International Conference on Intelligent Systems for Molecular Biology, vol.8, pp.93-103, 2000.

H. Cho and I. S. Dhillon, Coclustering of human cancer microarrays using minimum sum-squared residue coclustering, IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.5, issue.3, pp.385-400, 2008.

H. Cho, I. S. Dhillon, Y. Guan, and S. Sra, Minimum sum-squared residue co-clustering of gene expression data, Proceedings of the 2004 SIAM International Conference on Data Mining, pp.114-125, 2004.

J. P. Cunningham and Z. Ghahramani, Linear dimensionality reduction: Survey, insights, and generalizations, Journal of Machine Learning Research, vol.16, pp.2859-2900, 2015.

D. 'enza, A. I. Greenacre, and M. , Multiple correspondence analysis for the quantification and visualization of large categorical data sets, Advanced Statistical Methods for the Analysis of Large Data-Sets, pp.453-463, 2012.

I. Dhillon, Co-clustering documents and words using bipartite spectral graph partitioning, the 7th ACM SIGKDD, KDD '01, pp.269-274, 2001.

I. S. Dhillon, S. Mallela, and D. S. Modha, Information-theoretic co-clustering, Proceedings of the ninth international conference on Knowledge discovery and data mining, pp.89-98, 2003.

D. Ciaccio, A. Coli, M. Ibanez, and J. M. , Advanced Statistical Methods for the Analysis of Large Data-Sets: Studies in Theoretical and Applied Statistics, 2012.

C. Ding, T. Li, W. Peng, P. , and H. , Orthogonal nonnegative matrix t-factorizations for clustering, Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '06, p.23, 2006.

K. Eren, M. Deveci, O. Küçüktunç, and Ü. V. Çatalyürek, A comparative analysis of biclustering algorithms for gene expression data, Briefings in bioinformatics, vol.14, pp.279-92, 2013.

B. S. Everitt, S. Landau, M. Leese, and D. Stahl, , 2011.

M. Fagerland, S. Lydersen, L. , and P. , Statistical Analysis of Contingency Tables, 2017.

U. M. Fayyad and K. B. Irani, Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning, 13th International Joint Conference on Uncertainly in Artificial Intelligence(IJCAI93), pp.1022-1029, 1993.

F. Fessant, T. Benkhelif, C. , and F. , Anonymiser des données multidimensionnelles à l'aide du coclustering, 17ème Journées Francophones Extraction et Gestion des Connaissances, EGC 2017, pp.24-27, 2017.

, Janvier, p.121, 2017.

R. A. Fisher, The use of multiple measurements in taxonomic problems, Annals of Eugenics, vol.7, issue.2, pp.179-188, 1936.

A. Foss, M. Markatou, B. Ray, and A. Heching, A semiparametric method for clustering mixed data, Machine Learning, vol.105, pp.419-458, 2016.

E. Frank and I. H. Witten, Making better use of global discretization, Proceeding of 16th International Conference on Machine Learning, pp.115-123, 1999.

N. Friedman and M. Goldszmidt, Discretizing continuous attributes while learning bayesian networks, Proceedings of the Thirteenth International Conference on International Conference on Machine Learning, ICML'96, pp.157-165, 1996.

G. Getz, E. Levine, and E. Domany, Coupled two-way clustering analysis of gene microarray data, Proceedings of the National Academy of Sciences, vol.97, issue.22, pp.12079-12084, 2000.

I. J. Good, Categorization of classification, In Mathematics and Computer Science in Biology and Medicine, pp.115-125, 1965.

N. Govaert and M. , Latent block model for contingency table, Communications in Statistics, vol.39, issue.3, pp.416-425, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00447792

G. Govaert, Classification croisée, vol.17, 1983.

G. Govaert, Simultaneous clustering of rows and columns, Control and Cybernetics, vol.17, 1995.

G. Govaert and M. Nadif, Clustering with block mixture models, Pattern Recognition, vol.36, issue.2, p.26, 2003.

G. Govaert and M. Nadif, Clustering of contingency table and mixture model, European Journal of Operational Research, vol.183, issue.3, p.27, 2007.

G. Govaert and M. Nadif, Block clustering with Bernoulli mixture models: Comparison of different approaches, Computational Statistics and Data Analysis, vol.52, issue.6, p.26, 2008.

G. Govaert and M. Nadif, Un modèle de mélange pour la classification croisée d'un tableau de données continues. CAP'09, 11e conférence sur l'apprentissage artificiel, p.27, 2009.

G. Govaert and M. Nadif, Co-Clustering, vol.32, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00933301

M. J. Greenacre and J. Blasius, Multiple correspondence analysis and related methods, 2006.

R. Guigourès, M. Boullé, and F. Rossi, Discovering patterns in time-varying graphs: a triclustering approach, Advances in Data Analysis and Classification, vol.46, pp.1-28, 2015.

R. Guigourès, M. Boullé, and F. Rossi, Discovering patterns in time-varying graphs: a triclustering approach, Advances in Data Analysis and Classification, vol.87, pp.1-28, 2015.

J. A. Hartigan, Direct clustering of a data matrix, Journal of the American Statistical Association, vol.67, issue.337, pp.123-129, 1972.

J. A. Hartigan, Clustering Algorithms, p.13, 1975.

K. M. Ho and P. D. Scott, Zeta: A global method for discretization of continuous variables, 1997.

K. Ishiguro, N. Ueda, and H. Sawada, Subset infinite relational models, Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, vol.22, pp.547-555, 2012.

A. K. Jain, M. N. Murty, F. , and P. J. , Data clustering: A review, ACM Comput. Surv, vol.31, issue.3, pp.264-323, 1999.

C. Kemp, J. B. Tenenbaum, T. L. Griffiths, T. Yamada, and N. Ueda, Learning systems of concepts with an infinite relational model, Proceedings of the 21st National Conference on Artificial Intelligence, vol.1, pp.381-388, 2006.

C. Keribin, V. Brault, G. Celeux, G. , and G. , Estimation and selection for the latent block model on categorical data, 2013.
DOI : 10.1007/s11222-014-9472-2
URL : https://hal.archives-ouvertes.fr/hal-01256840

Y. Kluger, R. Basri, J. T. Chang, G. , and M. , Spectral biclustering of microarray cancer data: Co-clustering genes and conditions, 2003.

, Genome Research, vol.13, pp.703-716

R. Kohavi, Scaling up the accuracy of naive-bayes classifiers: A decision-tree hybrid, Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, KDD'96, pp.202-207, 1996.

H. W. Kuhn and B. Yaw, The hungarian method for the assignment problem, Naval Res. Logist. Quart, pp.83-97, 1955.

F. D. La-torre, A least-squares framework for component analysis, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.34, issue.6, pp.1041-1055, 2012.

C. Laclau, I. Redko, B. Matei, Y. Bennani, and V. Brault, Coclustering through Optimal Transport, Proceedings of the 34th International Conference on Machine Learning, vol.70, p.31, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01539101

D. D. Lee and H. S. Seung, Algorithms for non-negative matrix factorization, Advances in Neural Information Processing Systems, vol.13, p.12, 2011.

I. Lerman and H. Leredde, La méthode des pôles d'attraction, Journées Analyse des Données et Informatique, 1977.

M. Lichman, UCI machine learning repository, vol.52, p.100, 2013.

H. Liu, F. Hussain, C. L. Tan, and M. Dash, Discretization: An enabling technique, Data Min. Knowl. Discov, vol.6, issue.4, pp.393-423, 2002.

B. Long, Z. M. Zhang, Y. , and P. S. , Co-clustering by block value decomposition, Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining, 2005.
DOI : 10.1145/1081870.1081949

S. Madeira and A. Oliveira, Biclustering algorithms for biological data analysis: A survey, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2004.
DOI : 10.1109/tcbb.2004.2

O. Maimon and L. Rokach, Data Mining and Knowledge Discovery Handbook, 2010.

V. Mansinghka, P. Shafto, E. Jonas, C. Petschulat, M. Gasner et al., Crosscat: A fully bayesian nonparametric method for analyzing heterogeneous, high dimensional data, Journal of Machine Learning Research, vol.17, issue.138, pp.1-49, 2016.

V. K. Mansinghka, MIT Probabilistic Computing Project:CrossCat. (accessed last: October, vol.3, 2017.

G. Mclachlan and T. Krishnan, The EM Algorithm and Extensions. Wiley series in probability and statistics, p.27, 2008.

I. V. Mechelen, H. Bock, and P. D. Boeck, Two-mode clustering methods: astructuredoverview, Statistical Methods in Medical Research, vol.13, issue.5, pp.363-394, 2004.

T. Meeds and S. Roweis, Nonparametric bayesian biclustering, vol.30, p.29, 2007.

A. Mittal and L. Cheong, Employing discrete bayes error rate for discretization and feature selection tasks, 2002 IEEE International Conference on Data Mining, pp.298-305, 2002.

K. P. Murphy, Machine Learning: A Probabilistic Perspective, 2012.

M. Nadif and G. Govaert, Block clustering of contingency table and mixture model, Proceedings of the 6th International Conference on Advances in Intelligent Data Analysis, IDA'05, pp.249-259, 2005.

V. A. Padilha and R. J. Campello, A systematic comparative evaluation of biclustering techniques, BMC Bioinformatics, vol.18, issue.1, p.55, 2017.

J. Pitman, Combinatorial stochastic processes, Dept. of Statistics. UC, Berkeley, 2002.

B. Pontes, R. Giráldez, and J. S. Aguilar-ruiz, Biclustering on expression data: A review, Journal of Biomedical Informatics, vol.57, pp.163-180, 2015.

A. C. Rencher, Methods of Multivariate Analysis. Wiley Series in Probability and Statistics, vol.12, p.11, 2002.

G. Saporta, Probabilités, analyse des données et statistique. Editions Technip, vol.12, 2006.

B. Scholkopf and A. J. Smola, Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, 2001.

B. Schölkopf and A. J. Smola, Learning with kernels: Support vector machines, regularization, optimization, and beyond, IEEE Transactions on Neural Networks, vol.16, pp.781-781, 2002.

G. Schwarz, Estimating the dimension of a model, Ann. Statist, vol.6, issue.2, p.27, 1978.

H. Shan and A. Banerjee, Bayesian co-clustering, Proceedings of the 2008 Eighth IEEE International Conference on Data Mining, ICDM '08, pp.530-539, 2008.

H. Shan and A. Banerjee, Residual bayesian co-clustering for matrix approximation, SDM, 2010.

T. A. Snijders and K. Nowicki, Estimation and prediction for stochastic blockmodels for graphs with latent block structure, Journal of Classification, vol.14, issue.1, p.26, 1997.

L. Sun, S. Ji, Y. , and J. , A least squares formulation for a class of generalized eigenvalue problems in machine learning, Proceedings of the 26th Annual International Conference on Machine Learning, ICML '09, p.12, 2009.

A. Tanay, R. Sharan, and R. Shamir, Discovering statistically significant biclusters in gene expression data, Bioinformatics, vol.18, issue.1, pp.136-180, 2002.

A. Tanay, R. Sharan, and R. Shamir, Biclustering algorithms: A survey, Handbook of Computational Molecular Biology Edited by: Aluru S. Chapman & Hall/CRC Computer and Information Science Series, p.18, 2005.

C. Tang, L. Zhang, A. Zhang, and M. Ramanathan, Interrelated two-way clustering: an unsupervised approach for gene expression data analysis, Proceedings 2nd Annual IEEE International Symposium on Bioinformatics and Bioengineering, p.17, 2001.

N. Tishby, F. Pereira, and W. Bialek, The information bottleneck method. Invited paper to The 37th annual Allerton Conference on Communication, Control, and Computing, vol.17, 1999.

P. Wang, C. Domeniconi, K. B. Laskey, M. Grobelnik, D. Mladeni? et al., Latent dirichlet bayesian co-clustering, Machine Learning and Knowledge Discovery in Databases, pp.522-537, 2009.

P. Wang, C. Domeniconi, H. Rangwala, and K. B. Laskey, Feature enriched nonparametric bayesian co-clustering, PAKDD, 2012.

Z. Xu, V. Tresp, K. Yu, and H. Kriegel, Infinite hidden relational models. CoRR, abs/1206.6864, 2006.

J. Yang, H. Wang, W. Wang, Y. , and P. , Enhanced biclustering on expression data, Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings, pp.321-327, 2003.

W. H. Yang, D. Q. Dai, Y. , and H. , Finding correlated biclusters from gene expression data, IEEE Transactions on Knowledge and Data Engineering, vol.23, issue.4, pp.568-584, 2011.

J. Yoo and S. Choi, Orthogonal nonnegative matrix tri-factorization for co-clustering : Multiplicative updates on stiefel manifolds. Information processing & management, 2010.