C. Charu, J. L. Aggarwal, P. S. Wolf, C. Yu, J. S. Procopiuc et al., Fast algorithms for projected clustering, SIGMOD Rec, vol.28, issue.2, pp.61-72, 1999.

M. Ankerst, M. M. Breunig, H. Kriegel, and J. Sander, OPTICS, ACM SIGMOD Record, vol.28, issue.2, pp.49-60, 1999.
DOI : 10.1145/304181.304187

D. Arthur and S. Vassilvitskii, k-means++: The advantages of careful seeding, Proceedings of the eighteenth annual ACM?SIAM symposium on Discrete algorithms, 2007.

B. Bahmani, B. Moseley, A. Vattani, R. Kumar, and S. Vassilvitskii, Scalable k-means++, Proc. VLDB Endow, pp.622-633, 2012.
DOI : 10.14778/2180912.2180915

J. Buhler, Efficient large-scale sequence comparison by locality-sensitive hashing, Bioinformatics, vol.17, issue.5, pp.419-428, 2001.
DOI : 10.1093/bioinformatics/17.5.419
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.21.1356

R. Cilibrasi and P. M. Vitnyi, Clustering by Compression, IEEE Transactions on Information Theory, vol.51, issue.4, pp.1523-1545, 2005.
DOI : 10.1109/TIT.2005.844059
URL : http://arxiv.org/abs/cs/0312044

J. C. Dunn, A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters, Journal of Cybernetics, vol.3, issue.3, pp.32-57, 1973.
DOI : 10.1080/01969727308546046

M. Ester, H. Kriegel, S. Jörg, and X. Xu, A density-based algorithm for discovering clusters in large spatial databases with noise, Second International Conference on Knowledge Discovery and Data Mining, pp.226-231, 1996.

G. Hamerly and C. Elkan, Learning the k in k -means, Neural Information Processing Systems, 2003.

S. Har-peled and S. Mazumdar, Coresets for k-means and k-median clustering and their applications, pp.291-300, 2004.
DOI : 10.1145/1007352.1007400

J. A. Hartigan and M. A. Wong, Algorithm AS 136: A K-Means Clustering Algorithm, Applied Statistics, vol.28, issue.1, 1979.
DOI : 10.2307/2346830

K. Anil and . Jain, Data Clustering : 50 Years Beyond K-Means, Pattern Recognition Letters, 2009.

P. Stuart and . Lloyd, Least squares quantization in pcm, IEEE Transactions on Information Theory, vol.28, pp.129-137, 1982.

J. Macqueen, Some methods for classification and analysis of multivariate observations, Proc. 5th Berkeley Symp, pp.281-297, 1965.

A. Mccallum, K. Nigam, and L. H. Ungar, Efficient clustering of high-dimensional data sets with application to reference matching, Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '00, pp.169-178, 2000.
DOI : 10.1145/347090.347123

D. Pelleg and A. Moore, -means algorithms with geometric reasoning, Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '99, 1999.
DOI : 10.1145/312129.312248
URL : https://hal.archives-ouvertes.fr/jpa-00252210

D. Pelleg and A. Moore, X-means: Extending K-means with Efficient Estimation of the Number of Clusters, 2000.

J. Peter and . Rousseeuw, Silhouettes: A graphical aid to the interpretation and validation of cluster analysis, Journal of Computational and Applied Mathematics, vol.20, issue.0, pp.53-65, 1987.

A. Catherine, . Sugar, M. Gareth, and . James, Finding the number of clusters in a dataset, Journal of the American Statistical Association, vol.98, issue.463, pp.750-763, 2003.

. Robertl and . Thorndike, Who belongs in the family? Psychometrika, pp.267-276, 1953.

R. Tibshirani, G. Walther, and T. Hastie, Estimating the number of clusters in a data set via the gap statistic, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.63, issue.2, pp.411-423, 2001.
DOI : 10.1111/1467-9868.00293

M. N. Vrahatis, B. Boutsinas, P. Alevizos, and G. Pavlides, The New k-Windows Algorithm for Improving thek -Means Clustering Algorithm, Journal of Complexity, vol.18, issue.1, pp.375-391, 2002.
DOI : 10.1006/jcom.2001.0633
URL : http://doi.org/10.1006/jcom.2001.0633