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
UCI repository of machine learning databases
C Blake
http://www. ics. uci. edu/~ mlearn/MLRepository. html, 1998
70261998
Exact indexing of dynamic time warping
E Keogh, CA Ratanamahatana
Knowledge and information systems 7 (3), 358-386, 2005
29312005
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
22762003
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
18672001
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
16122003
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
15722007
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
15082008
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
14172001
Derivative dynamic time warping
EJ Keogh, MJ Pazzani
Proceedings of the 2001 SIAM international conference on data mining, 1-11, 2001
12712001
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
11082001
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
9182012
Time series shapelets: a new primitive for data mining
L Ye, E Keogh
Proceedings of the 15th ACM SIGKDD international conference on Knowledge …, 2009
9152009
Scaling up dynamic time warping for datamining applications
EJ Keogh, MJ Pazzani
Proceedings of the sixth ACM SIGKDD international conference on Knowledge …, 2000
9092000
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
8972005
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
8642017
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
8442004
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
8382013
The ucr time series classification archive
Y Chen, E Keogh, B Hu, N Begum, A Bagnall, A Mueen, G Batista
July, 2015
7852015
Towards parameter-free data mining
E Keogh, S Lonardi, CA Ratanamahatana
Proceedings of the tenth ACM SIGKDD international conference on Knowledge …, 2004
7662004
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
7581998
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Articles 1–20