R. Alqadah, C. K. Reddy, J. Hu, and H. F. Alqadah, Biclustering neighborhood-based collaborative filtering method for top-n recommender systems, Knowledge and Information Systems, vol.11, issue.8, pp.475-491, 2015.
DOI : 10.1016/j.knosys.2013.04.010

J. Barr and J. Cuzzell, Wound care clinical pathway: a conceptual model. Ostomy Wound Manage, pp.18-24, 1996.

B. Ben-dor03-]-amir-ben-dor, R. Chor, Z. Karp, and . Yakhini, Discovering local structure in gene expression data: the order-preserving submatrix problem, Journal of computational biology, vol.10, pp.3-4373, 2003.

R. Bordet, M. Lang, C. Dieu, N. Billon, and J. P. Duffet, Early results from a multi-component French public-private partnership initiative to improve participation in clinical research ??? CeNGEPS: a prospective before-after study, BMC Medical Research Methodology, vol.11, issue.48, p.67, 2015.
DOI : 10.3310/hta11480

R. Bordet, R. Ihl, A. D. Korczyn, G. Lanza, J. Jansa et al., Towards the concept of disease-modifier in post-stroke or vascular cognitive impairment: a consensus report, Boumaza80] Boumaza R. Contribution à l'étude descriptive d'une fonction aléatoire qualitative Thèse de 3ème cycle, 1980.
DOI : 10.2174/1871527315666160518124759

[. Busygin, O. Prokopyev, M. Panos, and . Pardalos, Biclustering in data mining, Computers & Operations Research, vol.35, issue.9, pp.2964-2987, 2008.
DOI : 10.1016/j.cor.2007.01.005

Y. Cheng, M. George, and . Church, Biclustering of expression data, Intelligent Systems for Molecular Biology, pp.93-103, 2000.

J. C. Deville, J. C. Deville83-]-deville, and G. Saporta, Correspondence analysis, with an extension towards nominal time series, Journal of Econometrics, vol.22, issue.1-2, pp.169-189, 1982.
DOI : 10.1016/0304-4076(83)90098-2

[. Dhaenens, L. Inderjit, S. Dhillon-ferraty, F. Vieu-pfisset14-]-benjamin, L. Fisset et al., Co-clustering documents and words using bipartite spectral graph partitioning Nonparameric Functional Data Analysis, Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining Clarisse Dhaenens. A framework for multiobjective Clustering, Conference on Metaheuristics and Nature Inspired Computing, pp.978-1848218062, 2001.

P. G. Heijden97-]-heijden, J. Teunissen, and C. Van-orle, Multiple Correspondence Analysis as a Tool for Quantification or Classification of Career Data, Journal of Educational and Behavioral Statistics, vol.22, issue.4, pp.447-477, 1997.
DOI : 10.3102/10769986022004447

J. Jacques, J. Taillard, D. Delerue, C. Dhaenens, L. Pontes et al., Conception of a dominance-based multi-objective local search in the context of classification rule mining in large and imbalanced data sets, Preda98] Preda C., Analyse harmonique qualitative des processus markoviens de sauts stationnaires, Scientific Annals of Alexandru Ioan Cuza University of Iasi (Romania), Computer Science SectionPreda14a] Cristian Preda Categorical Functional Data and Space-time clusters of lymphoma in northern France ?a scan statistics approach, Symposium on Modern Biotechnological Advances for Human Health ? BAHHPreda14b] Cristian Preda, pp.705-720163, 1998.
DOI : 10.1016/j.asoc.2015.06.002
URL : https://hal.archives-ouvertes.fr/hal-01216483