A. Schmutz, J. Jacques, C. Bouveyron, L. Chèze, and P. Martin, Clustering multivariate functional data in group-specific functional subspaces, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01652467

G. James and C. Et-sugar, Clustering for Sparsely Sampled Functional Data, Journal of the American Statistical Association, vol.98, issue.462, pp.397-408, 2003.
DOI : 10.1198/016214503000189
URL : http://www-rcf.usc.edu/~gareth/research/fclust.pdf

T. Tarpey and K. Et-kinateder, Clustering Functional Data, Journal of Classification, vol.20, issue.1, pp.93-114, 2003.
DOI : 10.1007/s00357-003-0007-3

J. M. Chiou and P. L. Li, Functional clustering and identifying substructures of longitudinal data, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.1, issue.4, pp.679-699, 2007.
DOI : 10.1093/bioinformatics/17.9.763

C. Bouveyron and J. Et-jacques, Model-based clustering of time series in groupspecific functional subspaces, Advances in Data Analysis and Classification, pp.281-300, 2011.

J. Jacques and C. Et-preda, Funclust: A curves clustering method using functional random variables density approximation, Neurocomputing, vol.112, pp.164-171, 2013.
DOI : 10.1016/j.neucom.2012.11.042
URL : https://hal.archives-ouvertes.fr/hal-00628247

C. Bouveyron, E. Come, and J. Et-jacques, The discriminative functional mixture model for a comparative analysis of bike sharing systems, The Annals of Applied Statistics, vol.9, issue.4, pp.1726-1760, 2015.
DOI : 10.1214/15-AOAS861
URL : https://hal.archives-ouvertes.fr/hal-01024186

C. Bouveyron, L. Bozzi, J. Jacques, and F. Et-jollois, The Functional Latent Block Model for the Co-Clustering of Electricity Comsumption Curves, Journal of the Royal Statistical Society, Series C, 2018.

M. Yamamoto, Clustering of Functional Data in a Low-Dimensional Subspace, Advances in Data Analysis and Classification, pp.219-247, 2012.

J. Jacques and C. Et-preda, Model-based clustering for multivariate functional data, Computational Statistics & Data Analysis, vol.71, pp.164-171, 2014.
DOI : 10.1016/j.csda.2012.12.004
URL : https://hal.archives-ouvertes.fr/hal-00943732

M. Yamamoto and Y. Terada, Functional factorial <mml:math altimg="si9.gif" display="inline" overflow="scroll" xmlns:xocs="http://www.elsevier.com/xml/xocs/dtd" xmlns:xs="http://www.w3.org/2001/XMLSchema" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://www.elsevier.com/xml/ja/dtd" xmlns:ja="http://www.elsevier.com/xml/ja/dtd" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:tb="http://www.elsevier.com/xml/common/table/dtd" xmlns:sb="http://www.elsevier.com/xml/common/struct-bib/dtd" xmlns:ce="http://www.elsevier.com/xml/common/dtd" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:cals="http://www.elsevier.com/xml/common/cals/dtd" xmlns:sa="http://www.elsevier.com/xml/common/struct-aff/dtd"><mml:mi>K</mml:mi></mml:math>-means analysis, Computational Statistics & Data Analysis, vol.79, pp.133-148, 2014.
DOI : 10.1016/j.csda.2014.05.010

M. Yamamoto and H. Hwang, Dimension-Reduced Clustering of Functional Data via Subspace Separation, Journal of Classification, vol.79, issue.2, pp.294-326, 2017.
DOI : 10.1016/j.csda.2014.05.010

F. Ferraty and P. Vieu, Additive prediction and boosting for functional data, Computational Statistics & Data Analysis, vol.53, issue.4, pp.1400-1413, 2009.
DOI : 10.1016/j.csda.2008.11.023
URL : https://hal.archives-ouvertes.fr/hal-00628614