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Eamonn Keogh
Eamonn Keogh
Distinguished Professor of Computer Science, University of California - Riverside
Verified email at cs.ucr.edu - Homepage
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
30232005
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
24182003
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
19102001
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
16812003
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
16792007
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
15802008
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
14802001
Derivative dynamic time warping
EJ Keogh, MJ Pazzani
Proceedings of the 2001 SIAM international conference on data mining, 1-11, 2001
13422001
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
11882001
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
10282017
Time series shapelets: a new primitive for data mining
L Ye, E Keogh
Proceedings of the 15th ACM SIGKDD international conference on Knowledge …, 2009
10182009
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
10102012
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
9492005
Scaling up dynamic time warping for datamining applications
EJ Keogh, MJ Pazzani
Proceedings of the sixth ACM SIGKDD international conference on Knowledge …, 2000
9362000
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
9192013
The UCR time series classification archive
Y Chen, E Keogh, B Hu, N Begum, A Bagnall, A Mueen, G Batista
July, 2015
8552015
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
8482004
Towards parameter-free data mining
E Keogh, S Lonardi, CA Ratanamahatana
Proceedings of the tenth ACM SIGKDD international conference on Knowledge …, 2004
7862004
Probabilistic discovery of time series motifs
B Chiu, E Keogh, S Lonardi
Proceedings of the ninth ACM SIGKDD international conference on Knowledge …, 2003
7782003
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
7781998
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