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

G. Govaert, M. Nadif, and . Co-clustering, Computing Engineering series, 2013.

C. Biernacki, G. Celeux, and G. Govaert, Assessing a mixture model for clustering with the integrated completed likelihood, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.22, issue.7, pp.719-725, 2000.

M. Nadif and G. Govaert, Algorithms for model-based block gaussian clustering, DMIN'08, the 2008 International Conference on Data Mining, 2008.

P. S. Bhatia, S. Iovleff, and G. Govaert, blockcluster: An R package for model-based co-clustering, Journal of Statistical Software, vol.76, issue.9, pp.1-800, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01093554

C. Laclau and M. Nadif, Diagonal latent block model for binary data, Statistics and Computing, vol.27, issue.5, pp.1145-1163, 2017.

J. Jacques and C. Biernacki, Model-based co-clustering for ordinal data, Computational Statistics & Data Analysis, vol.123, issue.C, pp.101-115, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01383927

Y. B. Slimen, S. Allio, and J. Jacques, Model-based co-clustering for functional data, Neurocomputing, vol.291, pp.97-108, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01422756

C. Bouveyron, L. Bozzi, J. Jacques, and F. Jollois, The functional latent block model for the co-clustering of electricity consumption curves, Journal of the Royal Statistical Society: Series C (Applied Statistics), vol.67, issue.4, pp.810-897, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01533438

B. S. Everitt, Introduction to Latent Variable Models, 1984.

C. Biernacki, T. Deregnaucourt, and V. Kubicki, Model-based clustering with mixed/missing data using the new software MixtComp, CMStatistics 815 2015 (ERCIM 2015), 2015.
URL : https://hal.archives-ouvertes.fr/hal-01249829

M. Marbac, C. Biernacki, and V. Vandewalle, Model-based clustering of gaussian copulas for mixed data, Communications in Statistics -Theory and Methods, vol.46, issue.23
URL : https://hal.archives-ouvertes.fr/hal-00987760

D. Mcparland and I. Gormley, Model based clustering for mixed data, p.820

. Clustmd, Adv. Data Anal. Classif, vol.10, issue.2, pp.155-169, 2016.

C. Bouveyron, M. Fauvel, and S. Girard, Kernel discriminant analysis and clustering with parsimonious gaussian process models, Statistics and Computing, vol.25, issue.6, pp.1143-1162, 2015.
URL : https://hal.archives-ouvertes.fr/hal-00707056

A. K. Smilde, J. A. Westerhuis, and S. D. Jong, A framework for sequential 825 multiblock component methods, Journal of Chemometrics, vol.17, issue.6, pp.323-337, 2003.

A. Bouchareb, M. Boullé, and F. Rossi, Co-clustering de données mixtesà base des modèles de mélange, Actes de la 17ème Conférence Internationale Francophone sur l'Extraction et gestion des connaissances (EGC'2017, p.830

F. Grenoble, , pp.141-152, 2017.

M. Ailem, F. Role, and M. Nadif, Sparse poisson latent block model for document clustering, IEEE Transactions on Knowledge and Data Engineering, vol.29, issue.7, pp.1563-1576, 2017.

D. Mcparland, C. M. Phillips, L. Brennan, H. M. Roche, and I. C. Gormley, , p.835

, Clustering high-dimensional mixed data to uncover sub-phenotypes: joint analysis of phenotypic and genotypic data, Statistics in Medicine, vol.36, issue.28, pp.4548-4569, 2017.

G. Celeux, D. Chauveau, and J. Diebolt, Some stochastic versions of the em algorithm, Journal of Statistical Computation and Simulation, vol.55, 1996.

M. Selosse, J. Jacques, C. Biernacki, and F. Cousson-gélie, Analysing a qualityof-life survey by using a coclustering model for ordinal data and some dynamic implications, Journal of the Royal Statistical Society: Series C (Applied Statistics

R. J. Little and D. B. Rubin, Statistical Analysis with Missing Data, 1986.

A. R. Donders, G. J. Van-der-heijden, T. Stijnen, and K. G. Moons, Review: A gentle introduction to imputation of missing values, Journal of Clinical Epidemiology, vol.59, issue.10, pp.1087-1091, 2006.

V. Robert, Classification croisee pour l'analyse de bases de donnees de grandes dimensions de pharmacovigilance, 2017.
URL : https://hal.archives-ouvertes.fr/tel-01695568

A. P. Dempster, N. M. Laird, and D. B. Rubin, Maximum likelihood from incomplete data via the em algorithm, Journal of he Royal Statistical Society, 855 series B, vol.39, issue.1, pp.1-38, 1977.

V. Brault, Estimation et selection de modele pour le modele des blocs latents, Gilles Mathematiques Paris, vol.11, p.2014, 2014.
URL : https://hal.archives-ouvertes.fr/tel-01090340

A. Gelman and D. Rubin, Inference from iterative simulation using multiple 860 sequences, Statistical Science, vol.7, issue.4, pp.457-472, 1992.

G. Schwarz, Estimating the dimension of a model, The Annals of Statistics, vol.6, pp.461-464, 1978.

C. Keribin, V. Brault, G. Celeux, and G. Govaert, Estimation and Selection for the Latent Block Model on Categorical Data, p.865, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00802764

K. S. Jones, A statistical interpretation of term specificity and its application in retrieval, Journal of Documentation, vol.28, issue.1, pp.11-21, 1972.

A. Salah and M. Nadif, Directional co-clustering, Advances in Data Analysis and Classification, pp.1-30, 2018.

M. Ailem, F. Role, and M. Nadif, Model-based co-clustering for the effective handling of sparse data, pp.108-122, 2017.

M. Ailem, F. Role, and M. Nadif, Graph modularity maximization as an effective method for co-clustering text data, Know.-Based Syst, vol.109, 2016.

G. Lubke and B. Muthn, Applying multigroup confirmatory factor models for continuous outcomes to likert scale data complicates meaningful group comparisons, Structural Equation Modeling: A Multidisciplinary Journal, vol.11, issue.4, pp.514-534, 2004.

E. E. Malonebeach and S. H. Zarit, Dimensions of social support and social 880 conflict as predictors of caregiver depression, International Psychogeriatrics, vol.7, issue.1, pp.25-38, 1995.

A. S. Zigmond and R. P. Snaith, The hospital anxiety and depression scale, Acta Psychiatrica Scandinavica, vol.67, issue.6, pp.361-370, 1983.

C. Biernacki and J. Jacques, Model-Based Clustering of Multivariate Ordi-885 nal Data Relying on a Stochastic Binary Search Algorithm, Statistics and Computing, vol.26, issue.5, pp.929-943, 2016.

C. Biernacki and A. Lourme, Unifying data units and models in (co-)clustering, Adv. Data Anal. Classif, vol.13, issue.1, pp.7-31, 2019.
URL : https://hal.archives-ouvertes.fr/hal-01653881

G. Govaert and M. Nadif, Mutual information, phi-squared and model-based 890 co-clustering for contingency tables, Advances in Data Analysis and Classification, vol.12, issue.3, pp.455-488, 2018.

L. Hubert and P. Arabie, Comparing partitions, Journal of Classification, vol.2, issue.1, pp.193-218, 1985.