Eamonn Keogh
Eamonn Keogh
Professor of Computer Science, University of California - Riverside
Verified email at cs.ucr.edu - Homepage
TitleCited byYear
UCI repository of machine learning databases
C Blake
http://www. ics. uci. edu/~ mlearn/MLRepository. html, 1998
64991998
Exact indexing of dynamic time warping
E Keogh, CA Ratanamahatana
Knowledge and information systems 7 (3), 358-386, 2005
23952005
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
18472003
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
15372001
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
13702003
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
12162008
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
11882001
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
11632007
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
10032001
Derivative dynamic time warping
EJ Keogh, MJ Pazzani
Proceedings of the 2001 SIAM international conference on data mining, 1-11, 2001
9992001
Scaling up dynamic time warping for datamining applications
EJ Keogh, MJ Pazzani
Proceedings of the sixth ACM SIGKDD international conference on Knowledge …, 2000
7402000
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
7122005
Towards parameter-free data mining
E Keogh, S Lonardi, CA Ratanamahatana
Proceedings of the tenth ACM SIGKDD international conference on Knowledge …, 2004
6842004
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
6791998
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
6702004
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
6632012
Time series shapelets: a new primitive for data mining
L Ye, E Keogh
Proceedings of the 15th ACM SIGKDD international conference on Knowledge …, 2009
6542009
Probabilistic discovery of time series motifs
B Chiu, E Keogh, S Lonardi
Proceedings of the ninth ACM SIGKDD international conference on Knowledge …, 2003
6432003
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
5972013
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
5972005
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