Eamonn Keogh - Google Scholar Citations

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Stefano LonardiProfessor of Computer Science, University of California, RiversideVerified email at cs.ucr.edu
Jessica LinGeorge Mason UniversityVerified email at gmu.edu
Chotirat Ann RatanamahatanaAssociate Professor, Dept. of Computer Engineering, Chulalongkorn UniversityVerified email at chula.ac.th
Abdullah MueenAssociate Professor, Department of Computer Science, University of New MexicoVerified email at cs.unm.edu
Michael J. PazzaniProfessor of Computer Science and Engineering, University of California, RiversideVerified email at UCR.edu
Gustavo Enrique de Almeida Prado Al...Associate Professor, CSE, UNSW SydneyVerified email at cse.unsw.edu.au
Thanawin RakthanmanonDept. of Computer Engineering, Kasetsart University, ThailandVerified email at ku.ac.th
Michalis VlachosProfessor of Information Systems, Faculty of Business and Economics, University of LausanneVerified email at unil.ch
Qiang ZhuIntuitVerified email at cs.ucr.edu
Yanping ChenUniversity of California, RiversideVerified email at ucr.edu
Bing HuFacebookVerified email at ucr.edu
Tony BagnallProfessor of Computer Science, University of East AngliaVerified email at uea.ac.uk
Nurjahan BegumUniversity of California, RiversideVerified email at cs.ucr.edu
Yan ZhuPhD Candidate in Computer Science, University of California, RiversideVerified email at ucr.edu
Chin-Chia Michael YehUniversity of California, RiversideVerified email at ucr.edu
Ada Wai-Chee FuChinese University of Hong KongVerified email at cse.cuhk.edu.hk
Bill ChiuVerified email at tapiama.org
Jesin ZakariaPhD candidateVerified email at ucr.edu
Selina ChuResearch Scientist, Nasa Jet Propulsion Laboratory, California Institute of TechnologyVerified email at jpl.nasa.gov
Sang-Hee LeeProfessor of Anthropology, UC RiversideVerified email at ucr.edu

Eamonn Keogh

Professor of Computer Science, University of California - Riverside

Verified email at cs.ucr.edu - Homepage

Data Mining Time Series Time Series Mining


Title	Cited by	Year
UCI repository of machine learning databases C Blake http://www. ics. uci. edu/~ mlearn/MLRepository. html, 1998	6438	1998
Exact indexing of dynamic time warping E Keogh, CA Ratanamahatana Knowledge and information systems 7 (3), 358-386, 2005	2334	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	1781	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	1493	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	1339	2003
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	1188	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	1169	2001
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	1110	2007
Locally adaptive dimensionality reduction for indexing large time series databases E Keogh, K Chakrabarti, M Pazzani, S Mehrotra ACM Sigmod Record 30 (2), 151-162, 2001	983	2001
Derivative dynamic time warping EJ Keogh, MJ Pazzani Proceedings of the 2001 SIAM international conference on data mining, 1-11, 2001	974	2001
Scaling up dynamic time warping for datamining applications EJ Keogh, MJ Pazzani Proceedings of the sixth ACM SIGKDD international conference on Knowledge …, 2000	725	2000
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	691	2005
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	667	1998
Towards parameter-free data mining E Keogh, S Lonardi, CA Ratanamahatana Proceedings of the tenth ACM SIGKDD international conference on Knowledge …, 2004	665	2004
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	646	2004
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	633	2012
Time series shapelets: a new primitive for data mining L Ye, E Keogh Proceedings of the 15th ACM SIGKDD international conference on Knowledge …, 2009	627	2009
Probabilistic discovery of time series motifs B Chiu, E Keogh, S Lonardi Proceedings of the ninth ACM SIGKDD international conference on Knowledge …, 2003	623	2003
Clustering of time-series subsequences is meaningless: implications for previous and future research E Keogh, J Lin Knowledge and information systems 8 (2), 154-177, 2005	573	2005
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	562	2013

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