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
Cross-words reference template for DTW-based speech recognition sys-tems, In: Proc. TENCON, vol.2, issue.35, pp.1576-1579, 2003. ,
DOI : 10.1109/tencon.2003.1273186
An Integrated Framework for Simultaneous Classification and Regression of Time-Series Data, SIAM International Conference on Data Mining, pp.653-664, 2010. ,
DOI : 10.1137/1.9781611972801.57
K-means++: The Advantages of Careful Seeding, Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, pp.1027-1035, 2007. ,
Sequence Kernels for Clustering and Visualizing Near Duplicate Video Segments, p.2012 ,
DOI : 10.1109/CVPR.2007.383226
Refining Initial Points for K-Means Clustering, Proceedings of the Fifteenth International Conference on Machine Learning. ICML '98, pp.91-99, 1998. ,
ISODATA: A novel method of data analysis and pattern classification, 1965. ,
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
Motion signal processing, Proceedings of the 22nd annual conference on Computer graphics and interactive techniques , SIGGRAPH '95, pp.97-104, 1995. ,
DOI : 10.1145/218380.218421
Classification of evoked potentials by Pearsonís correlation in a Brain-Computer Interface, In: Modelling C Automatic Control (theory and applications), vol.67, pp.156-166, 2007. ,
Aligning non-overlapping sequences, International Journal of Computer Vision, vol.48, issue.1, pp.39-51, 2002. ,
DOI : 10.1023/A:1014803327923
URL : http://www-cvr.ai.uiuc.edu/kriegman-grp/classes/cs491kp/13_03.PDF
Multivariate data analysis, J. Wiley, 1971. ,
The Multiple Sequence Alignment Problem in Biology, SIAM Journal on Applied Mathematics, vol.48, issue.5, pp.1073-1082, 1988. ,
DOI : 10.1137/0148063
T-Coffee: A novel method for fast and accurate multiple sequence alignment, In: Journal of molecular biology, vol.3021, pp.205-217, 2000. ,
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
Fast global alignment kernels, Proceedings of the 28th International Conference on Machine Learning, pp.929-936, 2011. ,
Classification trees for time series, Pattern Recognition, vol.45, issue.3, pp.1076-1091, 2012. ,
DOI : 10.1016/j.patcog.2011.08.018
URL : https://hal.archives-ouvertes.fr/hal-00739066
Adaptive dissimilarity index for measuring time series proximity, Advances in Data Analysis and Classification, vol.100, issue.441, pp.5-21, 2007. ,
DOI : 10.1093/aje/126.2.310
URL : https://hal.archives-ouvertes.fr/hal-00361046
Model-Based Gaussian and Non-Gaussian Clustering, Biometrics, vol.49, issue.3, pp.803-821, 1993. ,
DOI : 10.2307/2532201
Kernel k-means: spectral clustering and normalized cuts, Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, pp.551-556, 2004. ,
Trainging invariant support vector machines, In: Machine learning, vol.46, 2002. ,
Evaluation of the performance of clustering algorithms in kernel-induced feature space, Pattern Recognition, vol.38, issue.4, pp.607-611, 2005. ,
DOI : 10.1016/j.patcog.2004.09.006
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
Multiple sequence alignment with hierarchical clustering, Nucleic Acids Research, vol.16, issue.22, 1988. ,
DOI : 10.1093/nar/16.22.10881
URL : https://academic.oup.com/nar/article-pdf/16/22/10881/7047241/16-22-10881.pdf
Learning Multiple Temporal Matching for Time Series Classification, pp.198-209, 2013. ,
DOI : 10.1007/978-3-642-41398-8_18
URL : https://hal.archives-ouvertes.fr/hal-00881159
How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis, The Computer Journal, vol.41, issue.8, pp.578-588, 1998. ,
DOI : 10.1093/comjnl/41.8.578
URL : http://comjnl.oxfordjournals.org/cgi/reprint/41/8/578.pdf
A review on time series data mining, Engineering Applications of Artificial Intelligence, vol.24, issue.1, pp.164-181, 2011. ,
DOI : 10.1016/j.engappai.2010.09.007
Nonlinear alignment and averaging for estimating the evoked potential, IEEE Transactions on Biomedical Engineering, vol.43, issue.4, pp.348-356, 1996. ,
DOI : 10.1109/10.486255
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
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
CURE, ACM SIGMOD Record, vol.27, issue.2, pp.73-84, 1998. ,
DOI : 10.1145/276305.276312
The elements of statistical learning: data mining, inference and prediction, In: The Mathematical Intelligencer, vol.272, pp.83-85, 2005. ,
Yannis Batistakis, and Michalis Vazirgiannis On Clustering Validation Techniques, Journal of Intelligent Information Systems, vol.17, issue.2/3, pp.107-145, 2001. ,
DOI : 10.1023/A:1012801612483
Origins and extensions of the k-means algorithm in cluster analysis, In: Journal Electronique d'Histoire des Probabilités et de la Statistique Electronic Journal for History of Probability and Statistics, vol.4, p.49, 2008. ,
Data Mining, 2001. ,
DOI : 10.1145/233269.233324
URL : https://hal.archives-ouvertes.fr/hal-01534761
Tangent distance kernels for support vector machines, Object recognition supported by user interaction for service robots, p.16, 2002. ,
DOI : 10.1109/ICPR.2002.1048439
URL : http://www.kernel-machines.org/papers/upload_12617_TD-SVM.pdf
Time-series clustering by approximate prototypes, 2008 19th International Conference on Pattern Recognition, 2008. ,
DOI : 10.1109/ICPR.2008.4761105
URL : http://figment.cse.usf.edu/~sfefilat/data/papers/MoCT3.2.pdf
Preimage Problem in Kernel-Based Machine Learning, IEEE Signal Processing Magazine, vol.28, issue.2, pp.77-88, 2011. ,
DOI : 10.1109/MSP.2010.939747
Kernel methods in machine learning, The Annals of Statistics, vol.36, issue.3, pp.1171-1220, 2008. ,
DOI : 10.1214/009053607000000677
Minimum prediction residual principle applied to speech recognition In: Acoustics, Speech and Signal Processing, IEEE Transactions on, vol.231, issue.14, pp.67-72, 1975. ,
Stopping rules in principal components analysis: a comparison of heuristical and statistical approaches, In: Ecology, JSTOR, pp.2204-2214, 1993. ,
Temporal Integration for Audio Classification With Application to Musical Instrument Classification, IEEE Transactions on Audio, Speech, and Language Processing, vol.17, issue.1, pp.174-186, 2009. ,
DOI : 10.1109/TASL.2008.2007613
URL : http://perso.telecom-paristech.fr/~grichard/Publications/TSALP_joder08.pdf
Spatial clustering methods in data mining: A survey, In: Geographic Data Mining and Knowledge Discovery, 2001. ,
Computational Complexity of Multiple Sequence Alignment with SP-Score, Journal of Computational Biology, vol.8, issue.6, pp.615-623, 2001. ,
DOI : 10.1089/106652701753307511
URL : http://www.math.ohiou.edu/~just/PAPERS/newgap29.ps
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
On the need for time series data mining benchmarks, Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '02, pp.102-111, 2002. ,
DOI : 10.1145/775047.775062
The symmetric time warping algorithm: From continuous to discrete In Time Warps, String Edits and Macromolecules, pp.47-48, 1983. ,
Clustering of Time Series Subsequences is Meaningless: Implications for Previous and Future Research, Proceedings of the Third IEEE International Conference on Data Mining, ICDM '03, pp.115-162, 2003. ,
Scaling Up Dynamic Time Warping for Data Mining Applications, In: ACM SIGKDD, vol.15, issue.16, pp.285-289, 2000. ,
DOI : 10.1145/347090.347153
URL : http://www.cs.ucr.edu/~eamonn/kdd_2000.pdf
Clustering by means of Medoids, in Statistical Data Analysis Based on the L1?Norm and Related Methods, pp.405-416, 1987. ,
Finding Groups in Data. An Introduction to Cluster Analysis, pp.44-45, 1990. ,
Clustering of time series data???a survey, Pattern Recognition, vol.38, issue.11, pp.1857-1874, 2005. ,
DOI : 10.1016/j.patcog.2005.01.025
Multiple alignment of continuous time series, Proceeding's of the Neural Information Processing Systems. 2005 (cit, p.32 ,
An Overview of Temporal Data Mining, Proceedings of the 1st Australian Data Mining Workshop, 2002. ,
Geometric data analysis: from correspondence analysis to structured data analysis, 2004. ,
URL : https://hal.archives-ouvertes.fr/hal-00269083
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
Some methods for classification and analysis of multivariate observations, In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability Statistics, vol.1, issue.2, pp.281-297, 1967. ,
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, 2014. ,
DOI : 10.1109/TNNLS.2014.2333876
URL : https://hal.archives-ouvertes.fr/hal-00486916
A Comprehensive Approach to Clustering of Expressed Human Gene Sequence: The Sequence Tag Alignment and Consensus Knowledge Base, Genome Research, vol.9, issue.11, pp.1143-1155, 1999. ,
DOI : 10.1101/gr.9.11.1143
Data Mining and Knowledge Discovery Handbook, 2005. ,
Effecient and Effictive Clustering Methods for Spatial Data Mining, Proceeding's of the 20th VLDB Conference, 1994. ,
Dynamic time-alignment kernel in support vector machine In: Advances in neural information processing systems, p.921, 2002. ,
On Clustering Multimedia Time Series Data Using K-Means and Dynamic Time Warping, 2007 International Conference on Multimedia and Ubiquitous Engineering (MUE'07), pp.733-738, 2007. ,
DOI : 10.1109/MUE.2007.165
Shape averaging under Time Warping, 2009 6th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, pp.626-629, 2009. ,
DOI : 10.1109/ECTICON.2009.5137128
A Computer-Based Method of Selecting Clones for a Full-Length cDNA Project: Simultaneous Collection of Negligibly Redundant and Variant cDNAs, Genome Research, vol.12, issue.7, pp.1127-1134, 2002. ,
DOI : 10.1101/gr.75202
Contributions to the mathematical theory of evolution, In: Trans. R. Soc. Lond. Ser, pp.253-318, 1896. ,
Chat reaches 1 billion messages sent per day, p.2009 ,
A global averaging method for dynamic time warping, with applications to clustering, Pattern Recognition, vol.44, issue.3, pp.678-693, 2011. ,
DOI : 10.1016/j.patcog.2010.09.013
A Tutorial on Hidden Markov Models and selected applications in speech recognition, Proceedings of the IEEE 77, pp.257-286, 1989. ,
Objective criteria for the evaluation of clustering methods, In: Journal of the American Statistical association, vol.66336, pp.846-850, 1971. ,
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
Applied Dynamic Programming, 1962. ,
DOI : 10.1515/9781400874651
Fundamentals of speech recognition, 1993. ,
Learning Pedestrian Trajectories with Kernels, 2010 20th International Conference on Pattern Recognition, pp.149-152, 2010. ,
DOI : 10.1109/ICPR.2010.45
URL : http://disi.unitn.it/%7Ezen/data/icpr10_ricci.pdf
FastDTW: Toward Accurate Dynamic Time Warping in Linear Time and Space, In: KDD Workshop on Mining Temporal and Sequential Data, pp.70-80, 2004. ,
A dymanic programming approach to continuous speech recognition, Proceedings of the seventh International Congress on Acoustics, pp.65-69, 1971. ,
Dynamic programming algorithm optimization for spoken word recognition, IEEE Transactions on Acoustics, Speech, and Signal Processing, vol.261, pp.43-49, 1978. ,
DOI : 10.1109/tassp.1978.1163055
Dynamic time-alignment kernel in support vector machine, In: NIPS, vol.14, issue.58, pp.921-928, 2002. ,
K-Means-Type Algorithms: A Generalized Convergence Theorem and Characterization of Local Optimality In: Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.61, issue.48, pp.81-87, 1984. ,
Time warps, string edits, and macromolecules: the theory and practice of sequence comparison, pp.29-51, 1983. ,
Progressive and Iterative Approaches for Time Series Averaging, Proceedings of AALTD, p.2015 ,
URL : https://hal.archives-ouvertes.fr/hal-01208451
A Comparison of Progressive and Iterative Centroid Estimation Approaches Under Time Warp, In: Lecture Notes in Computer Science, Advanced Analysis and Learning on Temporal Data, vol.75, issue.4, pp.144-156, 2016. ,
DOI : 10.1016/j.patrec.2016.03.007
URL : https://hal.archives-ouvertes.fr/hal-01385018
Generalized k-means-based clustering for temporal data under weighted and kernel time warp, Pattern Recognition Letters, vol.75, issue.50, 2016. ,
DOI : 10.1016/j.patrec.2016.03.007
URL : https://hal.archives-ouvertes.fr/hal-01385059
A polynomial time solvable formulation of multiple sequence alignment, In: Journal of Computational Biology, vol.132, issue.33, pp.309-319, 2006. ,
CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice In: Nucleic acids research 22, pp.4673-4680, 1994. ,
Clustering Vessel Trajectories with Alignment Kernels under Trajectory Compression, Lecture Notes in Computer Science. Lecture Notes in Computer Science, vol.6321, pp.296-311, 2010. ,
DOI : 10.1007/978-3-642-15880-3_25
On the Complexity of Multiple Sequence Alignment, Journal of Computational Biology, vol.1, issue.4, pp.337-348, 1994. ,
DOI : 10.1089/cmb.1994.1.337
Supporting Content-Based Searches on Time Series via Approximation, Proceedings of the 12th International Conference on Scientific and Statistical Database Management. SS- DBM '00, pp.69-116, 2000. ,
Improved<tex>$hboxK$</tex>-Means Clustering Algorithm for Exploring Local Protein Sequence Motifs Representing Common Structural Property, IEEE Transactions on Nanobioscience, vol.4, issue.3, pp.255-265, 2005. ,
DOI : 10.1109/TNB.2005.853667
Applied Dynamic Programming, 2006. ,
Canonical time warping for alignment of human behavior In: Advances in neural information processing systems, pp.2286-2294, 2009. ,
Generalized time warping for multimodal alignment of human motion, 2012 IEEE Conference on. IEEE. 2012, pp.1282-1289 ,
Aligned cluster analysis for temporal segmentation of human motion In: Automatic Face & Gesture Recognition, FG'08. 8th IEEE International Conference on. IEEE, pp.1-7, 2008. ,
Hierarchical aligned cluster analysis for temporal clustering of human motion In: Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.353, pp.582-596, 2013. ,