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
64301998
Exact indexing of dynamic time warping
E Keogh, CA Ratanamahatana
Knowledge and information systems 7 (3), 358-386, 2005
23182005
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
17622003
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
14792001
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
13312003
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
11772008
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
11622001
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
10992007
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
9782001
Derivative dynamic time warping
EJ Keogh, MJ Pazzani
Proceedings of the 2001 SIAM international conference on data mining, 1-11, 2001
9662001
Scaling up dynamic time warping for datamining applications
EJ Keogh, MJ Pazzani
Proceedings of the sixth ACM SIGKDD international conference on Knowledge …, 2000
7172000
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
6832005
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
6651998
Towards parameter-free data mining
E Keogh, S Lonardi, CA Ratanamahatana
Proceedings of the tenth ACM SIGKDD international conference on Knowledge …, 2004
6612004
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
6432004
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
6292012
Probabilistic discovery of time series motifs
B Chiu, E Keogh, S Lonardi
Proceedings of the ninth ACM SIGKDD international conference on Knowledge …, 2003
6202003
Time series shapelets: a new primitive for data mining
L Ye, E Keogh
Proceedings of the 15th ACM SIGKDD international conference on Knowledge …, 2009
6142009
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
5692005
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
5482013
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