S. Albers, Online algorithms: a survey, Mathematical Programming, pp.3-26, 2003.
DOI : 10.1007/s10107-003-0436-0

A. Andoni and P. Indyk, Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions, FOCS'06. 47th Annual IEEE Symposium on, pp.459-468, 2006.

M. Bawa, T. Condie, and P. Ganesan, LSH forest, Proceedings of the 14th international conference on World Wide Web , WWW '05, pp.651-660, 2005.
DOI : 10.1145/1060745.1060840

J. S. Beis and D. G. Lowe, Shape indexing using approximate nearestneighbour search in high-dimensional spaces, Computer Vision and Pattern Recognition IEEE Computer Society Conference on, pp.1000-1006, 1997.
DOI : 10.1109/cvpr.1997.609451

J. L. Bentley, Multidimensional binary search trees used for associative searching, Communications of the ACM, vol.18, issue.9, pp.509-517, 1975.
DOI : 10.1145/361002.361007

E. Bernhardsson, Annoy. https://github.com/spotify/annoy, Last, vol.accessed, issue.2, pp.2017-2024

E. Bernhardsson, Benchmarking nearest neighbors. https://github. com/erikbern/ann-benchmarks. Last accessed, pp.2017-2024

S. Dasgupta and Y. Freund, Random projection trees and low dimensional manifolds, Proceedings of the fourtieth annual ACM symposium on Theory of computing, STOC 08, pp.537-546, 2008.
DOI : 10.1145/1374376.1374452
URL : http://www.cs.ucsd.edu/~dasgupta/papers/rptree-stoc.pdf

J. Fekete and R. Primet, Progressive analytics: A computation paradigm for exploratory data analysis. ArXiv e-prints, 2004.
URL : https://hal.archives-ouvertes.fr/hal-01361430

K. Fukunaga, Introduction to statistical pattern recognition. Academic press, 2013.

K. Hajebi, Y. Abbasi-yadkori, H. Shahbazi, and H. Zhang, Fast approximate nearest-neighbor search with k-nearest neighbor graph, IJCAI Proceedings-International Joint Conference on Artificial Intelligence, p.1312, 2011.

Y. Jia, J. Wang, G. Zeng, H. Zha, and X. Hua, Optimizing kd-trees for scalable visual descriptor indexing, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.3392-3399, 2010.
DOI : 10.1109/CVPR.2010.5540006

B. Kulis and K. Grauman, Kernelized locality-sensitive hashing for scalable image search, 2009 IEEE 12th International Conference on Computer Vision, pp.2130-2137, 2009.
DOI : 10.1109/ICCV.2009.5459466

B. Leibe, K. Mikolajczyk, and B. Schiele, Efficient Clustering and Matching for Object Class Recognition, Procedings of the British Machine Vision Conference 2006, pp.789-798, 2006.
DOI : 10.5244/C.20.81
URL : http://www.vision.ee.ethz.ch/~bleibe/papers/leibe-efficientclustering-bmvc06.pdf

Q. Lv, W. Josephson, Z. Wang, M. Charikar, and K. Li, Multi-probe LSH: efficient indexing for high-dimensional similarity search, Proceedings of the 33rd international conference on Very large data bases, pp.950-961, 2007.

Y. Malkov, A. Ponomarenko, A. Logvinov, and V. Krylov, Approximate nearest neighbor algorithm based on navigable small world graphs, Information Systems, vol.45, pp.61-68, 2014.
DOI : 10.1016/j.is.2013.10.006

Y. A. Malkov and D. Yashunin, Efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs. arXiv preprint
DOI : 10.1016/j.is.2013.10.006

. Mrpt-performance-comparison, https://github.com/ejaasaari/ mrpt-comparison. Last accessed, pp.2017-2024

M. Muja, Flann -fast library for approximate nearest neighbors. https: //github.com/mariusmuja/flann, Last, vol.accessed, issue.2, pp.2017-2024

M. Muja and D. G. Lowe, Fast approximate nearest neighbors with automatic algorithm configuration, pp.331-340, 2009.

M. Muja and D. G. Lowe, Scalable nearest neighbor algorithms for high dimensional data. Pattern Analysis and Machine Intelligence, IEEE Transactions, issue.2
DOI : 10.1109/tpami.2014.2321376
URL : http://ieeexplore.ieee.org:80/stamp/stamp.jsp?tp=&arnumber=6809191

J. Nielsen, Usability engineering, 1994.

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion et al., Scikit-learn: Machine learning in Python, Journal of Machine Learning Research, vol.12, issue.1 2, pp.2825-2830, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00650905

J. Pennington, R. Socher, and C. D. Manning, Glove: Global Vectors for Word Representation, Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp.1532-1543, 2014.
DOI : 10.3115/v1/D14-1162

N. Pezzotti, B. P. Lelieveldt, L. Van-der-maaten, T. Höllt, E. Eisemann et al., Approximated and User Steerable tSNE for Progressive Visual Analytics, IEEE Transactions on Visualization and Computer Graphics, vol.23, issue.7, pp.1739-1752, 2017.
DOI : 10.1109/TVCG.2016.2570755
URL : http://arxiv.org/abs/1512.01655

. Rpforest, https://github.com/lyst/rpforest, Last, vol.accessed, issue.2, pp.2017-2024

T. B. Sebastian and B. B. Kimia, Metric-based shape retrieval in large databases, Object recognition supported by user interaction for service robots, pp.291-296, 2002.
DOI : 10.1109/ICPR.2002.1047852
URL : http://www.lems.brown.edu/vision/publications/ICPR02.pdf

C. Silpa-anan and R. Hartley, Optimised KD-trees for fast image descriptor matching, 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp.1-8, 2008.
DOI : 10.1109/CVPR.2008.4587638
URL : http://cms.brookes.ac.uk/research/visiongroup/talks/hartley/kdtrees_camera_ready-2.pdf

R. F. Sproull, Refinements to nearest-neighbor searching ink-dimensional trees, Algorithmica, vol.3, issue.3, pp.579-589, 1991.
DOI : 10.1007/BF01759061

C. D. Stolper, A. Perer, and D. Gotz, Progressive Visual Analytics: User-Driven Visual Exploration of In-Progress Analytics, IEEE Transactions on Visualization and Computer Graphics, vol.20, issue.12, pp.1653-1662, 2014.
DOI : 10.1109/TVCG.2014.2346574

T. N. Tran, R. Wehrens, and L. M. Buydens, KNN-kernel density-based clustering for high-dimensional multivariate data, Computational Statistics & Data Analysis, vol.51, issue.2, pp.513-525, 2006.
DOI : 10.1016/j.csda.2005.10.001

J. Wang, J. Wang, G. Zeng, Z. Tu, R. Gan et al., Scalable k-NN graph construction for visual descriptors, 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp.1106-1113, 2012.
DOI : 10.1109/CVPR.2012.6247790