P. S. Bradley and U. M. Fayyad, Refining Initial Points for K-Means Clustering, ICML, pp.91-99, 1998.

M. Halkidi, Y. Batistakis, and M. Vazirgiannis, On Clustering Validation Techniques, Journal of Intelligent Information Systems, pp.107-145, 2001.

K. Kalpakis, D. Gada, and V. Puttagunta, Distance measures for effective clustering of ARIMA time-series, Proceedings 2001 IEEE International Conference on Data Mining, pp.273-280, 2001.
DOI : 10.1109/ICDM.2001.989529

E. Keogh, J. Lin, and W. Truppel, Clustering of time series subsequences is meaningless. Implications for Previous and Future Research, IEEE CS, 2003.

R. Moeckel and B. Murray, Measuring the distance between time series, Physica D: Nonlinear Phenomena, vol.102, issue.3-4, pp.187-194, 1997.
DOI : 10.1016/S0167-2789(96)00154-6

D. Gunopulos, Time series similarity measures, Encyclopedia of Biostatistics, pp.2-12074, 2004.

P. Marteau, Time Warp Edit Distance with Stiffness Adjustment for Time Series Matching, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.31, issue.2, p.76, 2008.
DOI : 10.1109/TPAMI.2008.76

URL : https://hal.archives-ouvertes.fr/hal-00135473

G. Batista, X. Wang, and E. J. Keogh, A Complexity-Invariant Distance Measure for Time Series, SDM, SIAM / Omnipress, p.60, 2011.
DOI : 10.1137/1.9781611972818.60

C. Cassisi, A. Pulvirenti, A. Cannata, M. Aliotta, and P. Montalto, Similarity Measures and Dimensionality Reduction Techniques for Time Series Data Mining, 2012.
DOI : 10.5772/49941

J. Serrà, L. Arcos, and J. , An Empirical Evaluation of Similarity Measures for Time Series Classification. CoRR, Knowledge-Based Systems 67, p.35, 2014.

X. Li, M. Makkie, B. Lin, S. Fazli, M. Davidson et al., Scalable Fast Rank-1 Dictionary Learning for fMRI Big Data Analysis, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '16, pp.511-519, 2016.
DOI : 10.1145/2517349.2522737

S. Soheily-khah, A. Douzal-chouakria, and E. Gaussier, Generalized k-means-based clustering for temporal data under weighted and kernel time warp, Pattern Recognition Letters, vol.75, pp.63-69, 2016.
DOI : 10.1016/j.patrec.2016.03.007

URL : https://hal.archives-ouvertes.fr/hal-01385059

A. Bagnall, J. Lines, A. Bostrom, J. Large, and E. Keogh, The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances, Data Mining and Knowledge Discovery, vol.10, issue.1???2, pp.606-660, 2017.
DOI : 10.1038/nmeth.2560

R. Bellman and S. Dreyfus, Applied Dynamic Programming, 1962.
DOI : 10.1515/9781400874651

F. Itakura, Minimum prediction residual principle applied to speech recognition, IEEE Transactions on Acoustics, Speech, and Signal Processing, vol.23, issue.1, pp.67-72, 1975.
DOI : 10.1109/TASSP.1975.1162641

J. B. Kruskall and M. Liberman, The symmetric time warping algorithm: From continuous to discrete, Time Warps, String Edits and Macromolecules, 1983.

S. Soheily-khah, Generalized k-means based clustering for temporal data under time warp, Theses, 2016.
DOI : 10.1016/j.patrec.2016.03.007

URL : https://hal.archives-ouvertes.fr/hal-01385059

K. Pearson, Contributions to the mathematical theory of evolution, Trans. R. Soc. Lond. Ser., Pages, pp.253-318, 1896.

B. D. Macarthur, A. Lachmann, I. R. Lemischka, and A. Ma-'ayan, GATE: software for the analysis and visualization of high-dimensional time series expression data, Bioinformatics, vol.26, issue.1, pp.143-144, 2010.
DOI : 10.1093/bioinformatics/btp628

J. Ernst, G. J. Nau, and Z. Bar-joseph, Clustering short time series gene expression data, Bioinformatics, vol.21, issue.Suppl 1, pp.159-168, 2005.
DOI : 10.1093/bioinformatics/bti1022

URL : https://academic.oup.com/bioinformatics/article-pdf/21/suppl_1/i159/525031/bti1022.pdf

Z. Abraham and P. Tan, An Integrated Framework for Simultaneous Classification and Regression of Time-Series Data, SIAM ICDM, pp.653-664, 2010.
DOI : 10.1137/1.9781611972801.57

F. Cabestaing, T. M. Vaughan, D. J. Mcfarland, and J. R. Wolpaw, Classification of evoked potentials by Pearsonís correlation in a Brain-Computer Interface, Modelling C Automatic Control (theory and applications), vol.67, pp.156-166, 2007.

J. Rydell, M. Borga, and H. Knutsson, Robust correlation analysis with an application to functional MRI, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, pp.453-456, 2008.
DOI : 10.1109/ICASSP.2008.4517644

URL : http://www.ami.imt.liu.se/Publications/pdfs/rbk08.pdf

A. Gaidon, Z. Harchaoui, and C. Schmid, A time series kernel for action recognition, Procedings of the British Machine Vision Conference 2011, 2011.
DOI : 10.5244/C.25.63

URL : https://hal.archives-ouvertes.fr/inria-00613089

H. Sakoe and S. Chiba, A dymanic programming approach to continuous speech recognition, Int. Congress on Acoustics, vol.3, pp.65-69, 1971.

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

E. J. Keogh and M. J. Pazzani, Scaling Up Dynamic Time Warping for Data Mining Applications, ACM SIGKDD, Pages, pp.285-289, 2000.
DOI : 10.1145/347090.347153

URL : http://www.cs.ucr.edu/~eamonn/kdd_2000.pdf

S. Salvador and C. Ph, FastDTW: Toward Accurate Dynamic Time Warping in Linear Time and Space, KDD Workshop on Mining Temporal and Sequential Data, pp.70-80, 2004.

C. Bahlmann, B. Haasdonk, and H. Burkhardt, Online handwriting recognition with support vector machines - a kernel approach, Proceedings Eighth International Workshop on Frontiers in Handwriting Recognition, pp.49-54, 2002.
DOI : 10.1109/IWFHR.2002.1030883

H. Shimodaira, K. I. Noma, M. Nakai, and S. Sagayama, Dynamic time-alignment kernel in support vector machine, NIPS, vol.14, pp.921-928, 2002.

M. Cuturi, J. Vert, . Ph, O. Birkenes, and T. Matsui, A Kernel for Time Series Based on Global Alignments, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07, pp.413-416, 2007.
DOI : 10.1109/ICASSP.2007.366260

URL : http://cbio.ensmp.fr/~jvert/publi/pdf/Cuturi2007Kernel.pdf

P. Marteau and S. Gibet, On Recursive Edit Distance Kernels With Application to Time Series Classification, IEEE Transactions on Neural Networks and Learning Systems, vol.26, issue.6, pp.1121-1133, 2015.
DOI : 10.1109/TNNLS.2014.2333876

URL : https://hal.archives-ouvertes.fr/hal-00486916

T. Hofmann, B. Scholkopf, and A. J. Smola, Kernel methods in machine learning, The Annals of Statistics, vol.36, issue.3, pp.1171-1220, 2008.
DOI : 10.1214/009053607000000677

C. Joder, S. Essid, and G. Richard, Alignment kernels for audio classification with application to music instrument recognition, European Signal Processing Conference EUSIPCO, pp.1-5, 2008.

G. Das, K. Lin, H. Mannila, G. Renganathan, and P. Smyth, Rule discovery from time series, Proceedings of th 4th International Conference of Knowledge Discovery and Data Mining, pp.16-22, 1998.

M. W. Kadous, Learning comprehensible descriptions of multivariate time series, 16th International Machine Learning Conference, pp.454-463, 1999.

J. J. Diez and C. A. Gonzalez, Applying boosting to similarity literals for time series classification, Multiple Classifier Systems, pp.210-219, 2000.

R. A. Ch and E. Keogh, Making Time-series Classification More Accurate Using Learned Constraints, pp.11-22, 2004.

M. Mller, Dynamic Time Warping Information Retrieval for Music and Motion, pp.978-981, 2007.

T. Rakthanmanon, Addressing Big Data Time Series, ACM Transactions on Knowledge Discovery from Data, vol.7, issue.3, p.2500489, 2013.
DOI : 10.1145/2513092.2500489

H. Izakian, W. Pedrycz, and I. Jamal, Fuzzy clustering of time series data using dynamic time warping distance, Engineering Applications of Artificial Intelligence, vol.39, p.15, 2015.
DOI : 10.1016/j.engappai.2014.12.015

B. K. Yi, H. Jagadish, and C. Faloutsos, Efficient retrieval of similar time sequences under time warping, ICDE, pp.23-27, 1998.

E. G. Caiani, A. Porta, G. Baselli, M. Turiel, S. Muzzupappa et al., Warped-average template technique to track on a cycle-by-cycle basis the cardiac filling phases on left ventricular volume, Computers in Cardiology 1998. Vol. 25 (Cat. No.98CH36292), pp.73-76, 1998.
DOI : 10.1109/CIC.1998.731723

J. Aach and G. Church, Aligning gene expression time series with time warping algorithms, Bioinformatics, vol.17, issue.6, pp.495-508, 2001.
DOI : 10.1093/bioinformatics/17.6.495

URL : https://academic.oup.com/bioinformatics/article-pdf/17/6/495/760358/170495.pdf

Z. Bar-joseph, G. Gerber, D. Gifford, T. Jaakkola, and I. Simon, A new approach to analyzing gene expression time series data, Proceedings of the sixth annual international conference on Computational biology , RECOMB '02, pp.39-48, 2002.
DOI : 10.1145/565196.565202

URL : http://www.psrg.lcs.mit.edu/pubs/BarGerGifJaa-recomb02.ps

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