Volgen
Eamonn Keogh
Eamonn Keogh
Distinguished Professor of Computer Science, University of California - Riverside
Geverifieerd e-mailadres voor cs.ucr.edu - Homepage
Titel
Geciteerd door
Geciteerd door
Jaar
Exact indexing of dynamic time warping
E Keogh, CA Ratanamahatana
Knowledge and information systems 7, 358-386, 2005
32552005
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
26242003
Dimensionality reduction for fast similarity search in large time series databases
E Keogh, K Chakrabarti, M Pazzani, S Mehrotra
Knowledge and information Systems 3, 263-286, 2001
20782001
Experiencing SAX: a novel symbolic representation of time series
J Lin, E Keogh, L Wei, S Lonardi
Data Mining and knowledge discovery 15, 107-144, 2007
19122007
On the need for time series data mining benchmarks: a survey and empirical demonstration
E Keogh, S Kasetty
Proceedings of the eighth ACM SIGKDD international conference on Knowledge …, 2002
17982002
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
17032008
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
15802001
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, 606-660, 2017
13812017
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
12452001
Time series shapelets: a new primitive for data mining
L Ye, E Keogh
Proceedings of the 15th ACM SIGKDD international conference on Knowledge …, 2009
12012009
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
11642012
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
10482005
Scaling up dynamic time warping for datamining applications
EJ Keogh, MJ Pazzani
Proceedings of the sixth ACM SIGKDD international conference on Knowledge …, 2000
10152000
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, 275-309, 2013
10022013
The UCR time series classification archive
Y Chen, E Keogh, B Hu, N Begum, A Bagnall, A Mueen, G Batista
July, 2015
9322015
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
9002004
Clustering of time-series subsequences is meaningless: implications for previous and future research
E Keogh, J Lin
Knowledge and information systems 8, 154-177, 2005
8242005
Towards parameter-free data mining
E Keogh, S Lonardi, CA Ratanamahatana
Proceedings of the tenth ACM SIGKDD international conference on Knowledge …, 2004
8222004
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
8161998
Probabilistic discovery of time series motifs
B Chiu, E Keogh, S Lonardi
Proceedings of the ninth ACM SIGKDD international conference on Knowledge …, 2003
8092003
Het systeem kan de bewerking nu niet uitvoeren. Probeer het later opnieuw.
Artikelen 1–20