A. Alamsyah and B. Nurriz, Monte carlo simulation and clustering for customer segmentation in business organization, 2017 3rd International Conference on Science and Technology -Computer (ICST), pp.104-109, 2017.

V. Hodge and J. Austin, A survey of outlier detection methodologies, Artificial Intelligence Review, vol.22, issue.2, pp.85-126, 2004.

I. Ordovás-pascual and J. Sánchez-almeida, A fast version of the k-means classification algorithm for astronomical applications, Astronomy & Astrophysics, vol.565, p.53, 2014.

G. James, D. Witten, T. Hastie, and R. Tibshirani, An introduction to statistical learning, vol.112, 2013.

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

M. D. Escobar, Estimating normal means with a dirichlet process prior, Journal of the American Statistical Association, vol.89, issue.425, pp.268-277, 1994.

D. Juery, C. Abraham, and B. Fontez, Classification bayésienne non supervisée de données fonctionnelles, Journal de la Société Française de Statistique, vol.155, issue.2, pp.185-201, 2014.

S. Prasad and L. M. Bruce, Limitations of principal components analysis for hyperspectral target recognition, IEEE Geoscience and Remote Sensing Letters, vol.5, issue.4, pp.625-629, 2008.

S. Aghabozorgi, A. S. Shirkhorshidi, and T. Y. Wah, Time-series clustering-a decade review, Information Systems, vol.53, pp.16-38, 2015.

E. Keogh and J. Lin, Clustering of time-series subsequences is meaningless: implications for previous and future research, Knowledge and Information Systems, vol.8, issue.2, pp.154-177, 2005.

M. L. García, R. García-ródenas, and A. G. Gómez, K-means algorithms for functional data, Neurocomputing, vol.151, pp.231-245, 2015.

J. Lin, E. Keogh, L. Wei, and S. Lonardi, Experiencing sax: a novel symbolic representation of time series, Data Mining and Knowledge Discovery, vol.15, issue.2, pp.107-144, 2007.

S. Soheily-khah, A. Douzal-chouakria, and E. Gaussier, Generalized k-means-based clustering for temporal data under weighted and kernel time warp, Pattern Recognition Letters, vol.75, pp.63-69, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01385059

Y. Cheng, T. Huang, and S. Yang, A clustering method for misaligned curves, 2018.

J. Macqueen, Some methods for classification and analysis of multivariate observations, Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol.1, pp.281-297, 1967.

K. Meguelati, B. Fontez, N. Hilgert, and F. Masseglia, Dirichlet process mixture models made scalable and effective by means of massive distribution, SAC: Symposium on Applied Computing, 2019.
URL : https://hal.archives-ouvertes.fr/hal-01999453

D. Lovell, R. P. Adams, and V. Mansingka, Parallel markov chain monte carlo for dirichlet process mixtures, Workshop on Big Learning, NIPS, 2012.

S. Williamson, A. Dubey, and E. Xing, Parallel markov chain monte carlo for nonparametric mixture models, International Conference on Machine Learning, pp.98-106, 2013.

R. Wang and D. Lin, Scalable estimation of dirichlet process mixture models on distributed data, Proceedings of the 26th International Joint Conference on Artificial Intelligence, ser. IJCAI'17, pp.4632-4639, 2017.

Q. Zhu, G. Batista, T. Rakthanmanon, and E. Keogh, A novel approximation to dynamic time warping allows anytime clustering of massive time series datasets, Proceedings of the 2012 SIAM international conference on data mining, pp.999-1010, 2012.

M. Zaharia, M. Chowdhury, M. J. Franklin, S. Shenker, and I. Stoica, Spark: Cluster computing with working sets, 2010.

L. A. Shepp, Radon-nikodym derivatives of gaussian measures, Ann. Math. Statist, vol.37, issue.2, pp.321-354, 1966.

C. Abraham, P. A. Cornillon, E. Matzner-løber, and N. Molinari, Unsupervised curve clustering using b-splines, Scandinavian Journal of Statistics, vol.30, issue.3, pp.581-595, 2003.

M. Fauvel, J. Chanussot, and J. A. Benediktsson, Kernel principal component analysis for the classification of hyperspectral remote sensing data over urban areas, EURASIP J. Adv. Signal Process, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00449436

K. Ø. Mikalsen, F. M. Bianchi, C. Soguero-ruiz, and R. Jenssen, Time series cluster kernel for learning similarities between multivariate time series with missing data, Pattern Recognition, vol.76, pp.569-581, 2018.

R. M. Dudley, Real analysis and probability. wadsworth & brooks, 1989.

C. Rasmussen and C. Williams, Gaussian Processes for Machine Learning, 2006.

M. Seeger, Gaussian processes for machine learning, International Journal of Neural Systems, vol.14, issue.02, pp.69-106, 2004.

R. M. Neal, Markov chain sampling methods for dirichlet process mixture models, Journal of Computational and Graphical Statistics, vol.9, issue.2, pp.249-265, 2000.

E. Parzen, Regression analysis of continuous parameter time series, 2010.

, Statistical inference on time series by hilbert space methods, i, Stanford Univ CA Applied Mathematics and Statistics Labs, 1959.

, Probability density functionals and reproducing kernel hilbert spaces, Proceedings of the Symposium on Time Series Analysis, vol.196, pp.155-169, 1963.

M. F. Driscoll, The signal-noise problem-a solution for the case that signal and noise are gaussian and independent, Journal of Applied Probability, vol.12, issue.1, pp.183-187, 1975.

A. W. Van-der, J. H. Vaart, and . Van-zanten, Reproducing kernel hilbert spaces of gaussian priors, Pushing the limits of contemporary statistics: contributions in honor of Jayanta K, pp.200-222, 2008.

A. Oya, J. Navarro-moreno, and J. C. Ruiz-molina, Numerical evaluation of reproducing kernel hilbert space inner products, IEEE Transactions on Signal Processing, vol.57, issue.3, pp.1227-1233, 2009.

J. Dean and S. Ghemawat, Mapreduce: Simplified data processing on large clusters, Commun. ACM, vol.51, issue.1, pp.107-113, 2008.

J. Shafer, S. Rixner, and A. L. Cox, The hadoop distributed filesystem: Balancing portability and performance, 2010.

N. Coffey and J. Hinde, Analyzing time-course microarray data using functional data analysis-a review, Statistical Applications in Genetics and Molecular Biology, vol.10, issue.1, 2011.

A. Berlinet and C. Thomas-agnan, Reproducing kernel Hilbert spaces in probability and statistics, 2011.

G. Saporta, Data analysis for numerical and categorical individual time-series, Applied Stochastic Models and Data Analysis, vol.1, issue.2, pp.109-119, 1985.


M. D. Escobar and M. West, Bayesian density estimation and inference using mixtures, Journal of the American Statistical Association, vol.90, issue.430, pp.577-588, 1995.

X. Meng, J. Bradley, B. Yavuz, E. Sparks, S. Venkataraman et al., Mllib: Machine learning in apache spark, The Journal of Machine Learning Research, vol.17, issue.1, pp.1235-1241, 2016.

N. X. Vinh, J. Epps, and J. Bailey, Information theoretic measures for clusterings comparison: Variants, properties, normalization and correction for chance, J. Mach. Learn. Res, vol.11, pp.2837-2854, 2010.