S. Lloyd, Least squares quantization in pcm, IEEE transactions on information theory, vol.28, issue.2, pp.129-137, 1982.

T. Zhang, R. Ramakrishnan, and M. Livny, Birch: an efficient data clustering method for very large databases, ACM Sigmod Record, vol.25, 1996.

M. Ankerst, M. M. Breunig, H. Kriegel, and J. Sander, Optics: ordering points to identify the clustering structure, ACM Sigmod record, vol.28, pp.49-60, 1999.

M. Ester, H. Kriegel, J. Sander, and X. Xu, A density-based algorithm for discovering clusters in large spatial databases with noise, in KDD, vol.96, issue.34, pp.226-231, 1996.

R. J. Campello, D. Moulavi, and J. Sander, Density-based clustering based on hierarchical density estimates, Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp.160-172, 2013.

F. Hausdorff, Grundzuge der Mengenlehre, vol.61, 1978.

M. Fréchet, Sur quelques points du calcul fonctionnel, Rendiconti del Circolo Matematico di Palermo (1884-1940), vol.22

P. Besse, B. Guillouet, J. Loubes, and R. François, Review and perspective for distance based trajectory clustering, 2015.

B. Guillouet, Apprentissage statistique: application au trafic routierà partir de données structurées et aux données massives, 2016.

A. Eckstein, Automated flight track taxonomy for measuring benefits from performance based navigation, 2009 Integrated Communications, Navigation and Surveillance Conference, pp.1-12, 2009.

F. Rehm, Clustering of flight tracks, AIAA Infotech@ Aerospace, p.3412, 2010.

M. Enriquez, Identifying temporally persistent flows in the terminal airspace via spectral clustering, Tenth USA/Europe Air Traffic Management Research and Development Seminar (ATM 2013), 2013.

M. C. Murça, R. Delaura, R. Hansman, R. Jordan, T. Reynolds et al., Trajectory clustering and classification for characterization of air traffic flows, 2016.

M. C. Murça, R. J. Hansman, L. Li, and P. Ren, Flight trajectory data analytics for characterization of air traffic flows: A comparative analysis of terminal area operations between New York, Hong Kong and Sao Paulo, Transportation Research Part C: Emerging Technologies, vol.97, pp.324-347, 2018.

, Trajectory clustering of air traffic flows around airports, vol.84

E. Min, X. Guo, Q. Liu, G. Zhang, J. Cui et al., A survey of clustering with deep learning: From the perspective of network architecture, IEEE Access, vol.6, pp.39-501, 2018.

E. Aljalbout, V. Golkov, Y. Siddiqui, and D. Cremers, Clustering with deep learning: taxonomy and new methods, p.1801, 2018.

J. Xie, R. Girshick, and A. Farhadi, Unsupervised deep embedding for clustering analysis, International conference on machine learning, pp.478-487, 2016.

X. Guo, X. Liu, E. Zhu, and J. Yin, Deep clustering with convolutional autoencoders, International Conference on Neural Information Processing, pp.373-382, 2017.

B. Yang, X. Fu, N. D. Sidiropoulos, and M. Hong, Towards kmeans-friendly spaces: Simultaneous deep learning and clustering, Proceedings of the 34th International Conference on Machine Learning, vol.70, pp.3861-3870, 2017.

M. Schäfer, M. Strohmeier, V. Lenders, I. Martinovic, and M. Wilhelm, Bringing up OpenSky: A large-scale ADS-B sensor network for research, Proceedings of the 13th international symposium on Information processing in sensor networks, pp.83-94, 2014.

X. Olive, traffic, a toolbox for processing and analysing air traffic data, Journal of Open Source Software, vol.4, p.1518, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02294354

X. Olive and L. Basora, Reference data sets for detection and identification of significant events in historical aircraft trajectory data, 2019.

X. Olive, J. Grignard, T. Dubot, and J. Saint-lot, Detecting Controllers' Actions in Past Mode S Data by Autoencoder-Based Anomaly Detection, Proceedings of the 8th SESAR Innovation Days, 2018.
URL : https://hal.archives-ouvertes.fr/hal-02338690

X. Olive and L. Basora, Identifying Anomalies in past en-route Trajectories with Clustering and Anomaly Detection Methods, Proceedings of the 13th USA/Europe Air Traffic Management Research and Development Seminar, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02345597

L. Basora, X. Olive, and T. Dubot, Recent advances in anomaly detection methods applied to aviation, Aerospace, vol.6, issue.11
URL : https://hal.archives-ouvertes.fr/hal-02470453

L. V. Maaten and G. Hinton, Visualizing data using t-SNE, Journal of machine learning research, vol.9, pp.2579-2605, 2008.