Eamonn Keogh

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Abdullah MueenAssociate Professor, Department of Computer Science, University of New MexicoVerified email at cs.unm.edu
Chotirat Ann RatanamahatanaAssociate Professor, Dept. of Computer Engineering, Chulalongkorn UniversityVerified email at chula.ac.th
Jessica LinGeorge Mason UniversityVerified email at gmu.edu
Stefano LonardiProfessor of Computer Science, University of California, RiversideVerified email at cs.ucr.edu
Michael J. PazzaniDistinguished ScientistVerified email at UCSD.edu
Gustavo Enrique de Almeida Prado Al...Associate Professor, School of Computer Science and Engineering, University of New South WalesVerified email at cse.unsw.edu.au
Yan ZhuPhD Candidate in Computer Science, University of California, RiversideVerified email at ucr.edu
Thanawin RakthanmanonDept. of Computer Engineering, Kasetsart University, ThailandVerified email at ku.ac.th
Chin-Chia Michael YehVisa ResearchVerified email at visa.com
Michalis VlachosProfessor of Information Systems, HEC, Faculty of Business and Economics, University of LausanneVerified email at unil.ch
Tony BagnallProfessor of Computer Science, University of East AngliaVerified email at uea.ac.uk
Bing HuFacebookVerified email at ucr.edu
Qiang ZhuIntuitVerified email at cs.ucr.edu
Yanping ChenUniversity of California, RiversideVerified email at ucr.edu
Diego Furtado SilvaAssistant Professor, Departamento de Computação - Universidade Federal de São CarlosVerified email at ufscar.br
Nurjahan BegumUniversity of California, RiversideVerified email at cs.ucr.edu
Hoang Anh DauMachine Learning Research Engineer, UberVerified email at uber.com
Zachary ZimmermanGoogleVerified email at google.com
Bill ChiuVerified email at tapiama.org
Jesin ZakariaPhDVerified email at ucr.edu

Eamonn Keogh

Distinguished Professor of Computer Science, University of California - Riverside

Verified email at cs.ucr.edu - Homepage

Data Mining Time Series Time Series Mining


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Exact indexing of dynamic time warping E Keogh, CA Ratanamahatana Knowledge and information systems 7 (3), 358-386, 2005	3023	2005
A symbolic representation of time series, with implications for streaming algorithms J Lin, E Keogh, S Lonardi, B Chiu Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining …, 2003	2418	2003
Dimensionality reduction for fast similarity search in large time series databases E Keogh, K Chakrabarti, M Pazzani, S Mehrotra Knowledge and information Systems 3 (3), 263-286, 2001	1910	2001
On the need for time series data mining benchmarks: a survey and empirical demonstration E Keogh, S Kasetty Data Mining and knowledge discovery 7 (4), 349-371, 2003	1681	2003
Experiencing SAX: a novel symbolic representation of time series J Lin, E Keogh, L Wei, S Lonardi Data Mining and knowledge discovery 15 (2), 107-144, 2007	1679	2007
Querying and mining of time series data: experimental comparison of representations and distance measures H Ding, G Trajcevski, P Scheuermann, X Wang, E Keogh Proceedings of the VLDB Endowment 1 (2), 1542-1552, 2008	1580	2008
An online algorithm for segmenting time series E Keogh, S Chu, D Hart, M Pazzani Proceedings 2001 IEEE international conference on data mining, 289-296, 2001	1480	2001
Derivative dynamic time warping EJ Keogh, MJ Pazzani Proceedings of the 2001 SIAM international conference on data mining, 1-11, 2001	1342	2001
Locally adaptive dimensionality reduction for indexing large time series databases E Keogh, K Chakrabarti, M Pazzani, S Mehrotra Proceedings of the 2001 ACM SIGMOD international conference on Management of …, 2001	1188	2001
The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances A Bagnall, J Lines, A Bostrom, J Large, E Keogh Data mining and knowledge discovery 31 (3), 606-660, 2017	1028	2017
Time series shapelets: a new primitive for data mining L Ye, E Keogh Proceedings of the 15th ACM SIGKDD international conference on Knowledge …, 2009	1018	2009
Searching and mining trillions of time series subsequences under dynamic time warping T Rakthanmanon, B Campana, A Mueen, G Batista, B Westover, Q Zhu, ... Proceedings of the 18th ACM SIGKDD international conference on Knowledge …, 2012	1010	2012
Hot sax: Efficiently finding the most unusual time series subsequence E Keogh, J Lin, A Fu Fifth IEEE International Conference on Data Mining (ICDM'05), 8 pp., 2005	949	2005
Scaling up dynamic time warping for datamining applications EJ Keogh, MJ Pazzani Proceedings of the sixth ACM SIGKDD international conference on Knowledge …, 2000	936	2000
Experimental comparison of representation methods and distance measures for time series data X Wang, A Mueen, H Ding, G Trajcevski, P Scheuermann, E Keogh Data Mining and Knowledge Discovery 26 (2), 275-309, 2013	919	2013
The UCR time series classification archive Y Chen, E Keogh, B Hu, N Begum, A Bagnall, A Mueen, G Batista July, 2015	855	2015
Segmenting time series: A survey and novel approach E Keogh, S Chu, D Hart, M Pazzani Data mining in time series databases, 1-21, 2004	848	2004
Towards parameter-free data mining E Keogh, S Lonardi, CA Ratanamahatana Proceedings of the tenth ACM SIGKDD international conference on Knowledge …, 2004	786	2004
Probabilistic discovery of time series motifs B Chiu, E Keogh, S Lonardi Proceedings of the ninth ACM SIGKDD international conference on Knowledge …, 2003	778	2003
An Enhanced Representation of Time Series Which Allows Fast and Accurate Classification, Clustering and Relevance Feedback. EJ Keogh, MJ Pazzani Kdd 98, 239-243, 1998	778	1998

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