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. ,
A survey of outlier detection methodologies, Artificial Intelligence Review, vol.22, issue.2, pp.85-126, 2004. ,
A fast version of the k-means classification algorithm for astronomical applications, Astronomy & Astrophysics, vol.565, p.53, 2014. ,
An introduction to statistical learning, vol.112, 2013. ,
Maximum likelihood from incomplete data via the em algorithm, Journal of the Royal Statistical Society. Series B, pp.1-38, 1977. ,
Estimating normal means with a dirichlet process prior, Journal of the American Statistical Association, vol.89, issue.425, pp.268-277, 1994. ,
Classification bayésienne non supervisée de données fonctionnelles, Journal de la Société Française de Statistique, vol.155, issue.2, pp.185-201, 2014. ,
Limitations of principal components analysis for hyperspectral target recognition, IEEE Geoscience and Remote Sensing Letters, vol.5, issue.4, pp.625-629, 2008. ,
Time-series clustering-a decade review, Information Systems, vol.53, pp.16-38, 2015. ,
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. ,
K-means algorithms for functional data, Neurocomputing, vol.151, pp.231-245, 2015. ,
Experiencing sax: a novel symbolic representation of time series, Data Mining and Knowledge Discovery, vol.15, issue.2, pp.107-144, 2007. ,
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
A clustering method for misaligned curves, 2018. ,
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. ,
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
Parallel markov chain monte carlo for dirichlet process mixtures, Workshop on Big Learning, NIPS, 2012. ,
Parallel markov chain monte carlo for nonparametric mixture models, International Conference on Machine Learning, pp.98-106, 2013. ,
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. ,
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. ,
Spark: Cluster computing with working sets, 2010. ,
Radon-nikodym derivatives of gaussian measures, Ann. Math. Statist, vol.37, issue.2, pp.321-354, 1966. ,
Unsupervised curve clustering using b-splines, Scandinavian Journal of Statistics, vol.30, issue.3, pp.581-595, 2003. ,
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
Time series cluster kernel for learning similarities between multivariate time series with missing data, Pattern Recognition, vol.76, pp.569-581, 2018. ,
Real analysis and probability. wadsworth & brooks, 1989. ,
Gaussian Processes for Machine Learning, 2006. ,
Gaussian processes for machine learning, International Journal of Neural Systems, vol.14, issue.02, pp.69-106, 2004. ,
Markov chain sampling methods for dirichlet process mixture models, Journal of Computational and Graphical Statistics, vol.9, issue.2, pp.249-265, 2000. ,
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.
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. ,
Reproducing kernel hilbert spaces of gaussian priors, Pushing the limits of contemporary statistics: contributions in honor of Jayanta K, pp.200-222, 2008. ,
Numerical evaluation of reproducing kernel hilbert space inner products, IEEE Transactions on Signal Processing, vol.57, issue.3, pp.1227-1233, 2009. ,
Mapreduce: Simplified data processing on large clusters, Commun. ACM, vol.51, issue.1, pp.107-113, 2008. ,
The hadoop distributed filesystem: Balancing portability and performance, 2010. ,
Analyzing time-course microarray data using functional data analysis-a review, Statistical Applications in Genetics and Molecular Biology, vol.10, issue.1, 2011. ,
Reproducing kernel Hilbert spaces in probability and statistics, 2011. ,
Data analysis for numerical and categorical individual time-series, Applied Stochastic Models and Data Analysis, vol.1, issue.2, pp.109-119, 1985. ,
,
Bayesian density estimation and inference using mixtures, Journal of the American Statistical Association, vol.90, issue.430, pp.577-588, 1995. ,
Mllib: Machine learning in apache spark, The Journal of Machine Learning Research, vol.17, issue.1, pp.1235-1241, 2016. ,
Information theoretic measures for clusterings comparison: Variants, properties, normalization and correction for chance, J. Mach. Learn. Res, vol.11, pp.2837-2854, 2010. ,