S. Bougeard, R package mbclusterwise, 2016.

C. Charles, Régression Typologique et Reconnaissance des Formes, 1977.

H. Chun and S. Keles, Sparse partial least squares for simultaneous dimension reduction and variable selection, Journal of the Royal Statistical Society -Series B, vol.72, pp.3-25, 2010.

F. ?-de-carvalho, G. Saporta, and D. Queiroz, A Clusterwise Center and Range Regression Model for Interval-Valued , COMPSTAT'2010, 19th International Conference on Computational Statistics, pp.461-468, 2010.

E. Diday, Introduction à l'analyse factorielle typologique, Revue de Statistique Appliquée, vol.22, pp.29-38, 1974.

C. Hennig, Models and methods for clusterwise linear regression, Classification in the Information Age, p.31, 1999.

C. ?-lê, D. Rossouw, C. Robert-granié, and P. Besse, A Sparse PLS for Variable Selection when Integrating Omics data, Statistical Applications in Genetics and Molecular Biology, vol.7, issue.1, p.35, 2008.

F. Leisch, FlexMix: A General Framework for Finite Mixture Models and Latent Class Regression in R, Journal of Statistical Software, issue.8, p.11, 2004.

B. Liquet, P. Lafaye-de-michaux, B. Hejblum, and R. Thiebaut, Group and sparse group partial least square approaches applied in genomics context, Bioinformatics, vol.32, issue.1, pp.35-42, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01288891

N. Niang, S. Bougeard, G. Saporta, H. Abdi, C. Neaples-?-preda et al., Clusterwise PLS regression on a stochastic process, vol.58, pp.99-108, 2005.

H. Späth, Clusterwise linear regression, Computing, vol.22, pp.367-373, 1979.

R. Tibshirani, Regression shrinkage and selection via the Lasso, Journal of the Royal Statistical Society, Series B, vol.58, pp.267-288, 1996.