A. Jain, Data clustering: 50 years beyond K-means, Pattern Recognition Letters, vol.31, issue.8, pp.651-666, 2010.
DOI : 10.1016/j.patrec.2009.09.011
URL : https://link.springer.com/content/pdf/10.1007%2F978-3-540-87479-9_3.pdf

M. Steinbach, G. Karypis, and V. Kumar, A comparison of document clustering techniques, KDD workshop on text mining, pp.525-526, 2000.

A. Thalamuthu, I. Mukhopadhyay, X. Zheng, and G. Tseng, Evaluation and comparison of gene clustering methods in microarray analysis, Bioinformatics, vol.99, issue.19, pp.2405-2412, 2006.
DOI : 10.1073/pnas.192159399
URL : https://academic.oup.com/bioinformatics/article-pdf/22/19/2405/18529614/btl406.pdf

J. Macqueen, Some Methods for classification and Analysis of Multivariate Observations, Proc. of Berkeley Symposium on Mathematical Statistics and Probability, 1967.

A. Banerjee, S. Merugu, I. Dhillon, and J. Ghosh, Clustering with Bregman Divergences, J Mach Learn Res, vol.6, pp.1705-1749, 2005.
DOI : 10.1137/1.9781611972740.22
URL : http://www.lans.ece.utexas.edu/~abanerjee/papers/04/sdm-breg.ps

I. Dhillon, S. Mallela, and R. Kumar, A Divisive Information Theoretic Feature Clustering Algorithm for Text Classification, J Mach Learn Res, vol.3, pp.1265-1287, 2003.

Y. Linde, A. Buzo, and R. Gray, An Algorithm for Vector Quantizer Design, IEEE Transactions on Communications, vol.28, issue.1, pp.84-95, 1980.
DOI : 10.1109/TCOM.1980.1094577

L. Kaufman and P. Rousseeuw, Finding groups in data: an introduction to cluster analysis, 1990.
DOI : 10.1002/9780470316801

R. Ng and J. Han, Efficient and Effective Clustering Methods for Spatial Data Mining, Proceedings of the 20th International Conference on Very Large Data Bases. VLDB '94, pp.144-155, 1994.

R. Duda, P. Hart, and D. Stork, Pattern Classification, 2001.

V. Vapnik, The Nature of Statistical Learning Theory, 1995.

M. Girolami and . Mercer, Mercer kernel-based clustering in feature space, IEEE Transactions on Neural Networks, vol.13, issue.3, pp.780-784, 2002.
DOI : 10.1109/TNN.2002.1000150
URL : http://www.dcs.gla.ac.uk/~girolami/Machine_Learning_Module_2006/week_8/Lectures/kern_km.pdf

I. Dhillon, Y. Guan, and B. Kulis, Weighted Graph Cuts without Eigenvectors A Multilevel Approach, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.29, issue.11, pp.1944-1957, 1115.
DOI : 10.1109/TPAMI.2007.1115
URL : http://www.cs.utexas.edu/~kulis/pubs/pami_multilevel.pdf

V. Luxburg and U. , A tutorial on spectral clustering, Statistics and Computing, vol.21, issue.1, pp.395-416, 2007.
DOI : 10.1017/CBO9780511810633

J. Shi and J. Malik, Normalized cuts and image segmentation, IEEE Transactions on pattern analysis and machine intelligence, vol.22, issue.8, pp.888-905, 2000.

V. Roth, J. Laub, M. Kawanabe, and J. Buhmann, Optimal cluster preserving embedding of nonmetric proximity data, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.25, issue.12, pp.1540-1551, 2003.
DOI : 10.1109/TPAMI.2003.1251147
URL : http://www.inf.ethz.ch/~vroth/roth_laub_01251147.pdf

R. Chitta, J. R. Havens, T. Jain, and A. , Approximate kernel k-means, Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '11, pp.895-903, 2011.
DOI : 10.1145/2020408.2020558

R. Zhang and A. Rudnicky, A large scale clustering scheme for kernel K-Means, Object recognition supported by user interaction for service robots, pp.289-292, 2002.
DOI : 10.1109/ICPR.2002.1047453

P. Bradley, U. Fayyad, and R. C. , Scaling Clustering Algorithms to Large Databases In: Knowledge Discovery and Data Mining, pp.9-15, 1998.
DOI : 10.1145/545151.545176

B. Kulis, S. Basu, I. Dhillon, and R. Mooney, Semi-supervised graph clustering, Proceedings of the 22nd international conference on Machine learning , ICML '05, pp.1-22, 2008.
DOI : 10.1145/1102351.1102409

Y. Chen, E. Keogh, B. Hu, N. Begum, A. Bagnall et al., The UCR Time Series Classification Archive, 2015.

H. Kuhn, The Hungarian method for the assignment problem, Naval Research Logistics Quarterly, vol.3, issue.1-2, pp.83-97, 1955.
DOI : 10.2140/pjm.1953.3.369

H. Ding, G. Trajcevski, P. Scheuermann, X. Wang, and E. Keogh, Querying and mining of time series data, Proceedings of the VLDB Endowment, vol.1, issue.2, pp.1542-1552, 2008.
DOI : 10.14778/1454159.1454226

X. Wang, A. Mueen, H. Ding, G. Trajcevski, P. Scheuermann et al., Experimental comparison of representation methods and distance measures for time series data, Data Mining and Knowledge Discovery, vol.15, issue.1, pp.275-309, 2013.
DOI : 10.1145/1143844.1143974

H. Sakoe and S. Chiba, Dynamic programming algorithm optimization for spoken word recognition, IEEE Transactions on Acoustics, Speech, and Signal Processing, vol.26, issue.1, pp.43-49, 1978.
DOI : 10.1109/TASSP.1978.1163055

D. Berndt and J. Clifford, Using Dynamic Time Warping to Find Patterns in Time Series, pp.359-370, 1994.

T. Rakthanmanon, B. Campana, A. Mueen, G. Batista, B. Westover et al., Addressing Big Data Time Series, ACM Transactions on Knowledge Discovery from Data, vol.7, issue.3, pp.1-1031, 2013.
DOI : 10.1145/2513092.2500489

G. Lanckriet, N. Cristianini, P. Bartlett, L. Ghaoui, and M. Jordan, Learning the Kernel Matrix with Semidefinite Programming, J Mach Learn Res, vol.5, pp.27-72, 2004.

A. Ng, M. Jordan, and Y. Weiss, On spectral clustering: Analysis and an algorithm. In: Advances in neural information processing systems, pp.849-856, 2002.