H. Akaike, A new look at the statistical model identification, IEEE Transactions on Automatic Control, vol.9, pp.716-739, 1974.

J. Berrendero, A. Justel, and M. Svarc, Principal components for multivariate func- 400

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.719-744, 2000.
DOI : 10.1109/34.865189

L. Birge and P. Massart, Minimal penalties for gaussian model selection. Probability theory and related fields, pp.33-73, 2007.
URL : https://hal.archives-ouvertes.fr/hal-00141376

C. Bouveyron, E. Come, and J. J. , The discriminative functional mixture model for a comparative analysis of bike sharing systems, The Annals of Applied Statistics, vol.9, issue.4, pp.1726-60, 2015.
DOI : 10.1214/15-AOAS861

URL : https://hal.archives-ouvertes.fr/hal-01024186

C. Bouveyron and J. J. , Model-based clustering of time series in groupspecific functional subspaces Advances in Data Analysis and Classification 410, pp.281-300, 2011.

R. Cattell, The Scree Test For The Number Of Factors, Multivariate Behavioral Research, vol.1, issue.2, pp.245-76, 1966.
DOI : 10.1207/s15327906mbr0102_10

L. Chen and C. Jiang, Multi-dimensional functional principal component analysis, Statistics and Computing, vol.20, issue.9, pp.1181-92, 2016.
DOI : 10.1198/jcgs.2011.10122

URL : http://arxiv.org/pdf/1510.04439

J. Chiou, Y. Chen, and Y. Yang, Multivariate functional principal component analysis: A normalization approach, Statistica Sinica, vol.24, pp.1571-96, 2014.
DOI : 10.5705/ss.2013.305

J. Chiou and P. Li, Functional clustering and identifying substructures of longitudinal data, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.1, issue.4, pp.679-99, 2007.
DOI : 10.1093/bioinformatics/17.9.763

URL : http://onlinelibrary.wiley.com/doi/10.1111/j.1467-9868.2007.00605.x/pdf

A. Dempster, N. Laird, and D. Rubin, Maximum likelihood from incomplete data via the EM algorithm, Journal of the Royal Statistical Society, vol.39, issue.1, pp.1-38, 1977.

F. Ferraty and P. Vieu, Curves discrimination: a nonparametric functional approach, Computational Statistics & Data Analysis, vol.44, issue.1-2, pp.161-73, 2003.
DOI : 10.1016/S0167-9473(03)00032-X

C. Fraley and A. Raftery, Model-Based Clustering, Discriminant Analysis and Den- 425

C. Happ and S. Greven, Multivariate Functional Principal Component Analysis for Data Observed on Different (Dimensional) Domains, Journal of the American Statistical Association, vol.24, 2015.
DOI : 10.1007/978-0-8176-8349-8

F. Ieva, A. Paganoni, D. Pigoli, and V. Vitelli, Multivariate functional clustering for the morphological analysis of electrocardiograph curves, Journal of the Royal Statistical Society: Series C (Applied Statistics), vol.27, issue.3, pp.401-419, 2013.
DOI : 10.1016/S0002-8703(44)90603-4

J. Jacques and C. Preda, Funclust: A curves clustering method using functional random variables density approximation, Neurocomputing, vol.112, pp.164-71, 2013.
DOI : 10.1016/j.neucom.2012.11.042

URL : https://hal.archives-ouvertes.fr/hal-00628247

J. Jacques and C. Preda, Model-based clustering for multivariate functional data, Computational Statistics & Data Analysis, vol.71, pp.92-106, 2014.
DOI : 10.1016/j.csda.2012.12.004

URL : https://hal.archives-ouvertes.fr/hal-00943732

G. James and C. 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

M. Kayano, K. Dozono, and S. Konishi, Functional Cluster Analysis via Orthonormal- 440

C. Preda, Regression models for functional data by reproducing kernel Hilbert spaces methods, Journal of Statistical Planning and Inference, vol.137, issue.3, pp.829-869, 2007.
DOI : 10.1016/j.jspi.2006.06.011

J. Ramsay and B. Silverman, Functional data analysis, 2005.

W. Rand, Objective Criteria for the Evaluation of Clustering Methods, Journal of the American Statistical Association, vol.15, issue.336, pp.846-50, 1971.
DOI : 10.1080/01621459.1963.10500845

G. Schwarz, Estimating the Dimension of a Model, The Annals of Statistics, vol.6, issue.2, pp.461-465, 1978.
DOI : 10.1214/aos/1176344136

A. Singhal and D. Seborg, Clustering multivariate time-series data, Journal of Chemometrics, vol.48, issue.8, pp.427-465, 2005.
DOI : 10.1002/cjce.5450780316

URL : http://www.chemengr.ucsb.edu/~ceweb/faculty/seborg/pdfs/Singhal_JChemometrics.pdf

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

S. Tokushige, H. Yadohisa, and K. Inada, Crisp and fuzzy k-means clustering algorithms for multivariate functional data, Computational Statistics, vol.15, issue.3, pp.1-460, 2007.
DOI : 10.5183/jjscs1988.15.2_319

M. Yamamoto, Clustering of Functional Data in a Low-Dimensional Subspace Advances in Data Analysis and Classification, pp.219-266, 2012.

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

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.133-181, 2014.
DOI : 10.1016/j.csda.2014.05.010