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
836 2012 Fast shapelets: A scalable algorithm for discovering time series shapelets T Rakthanmanon, E Keogh
proceedings of the 2013 SIAM International Conference on Data Mining, 668-676, 2013
339 2013 Addressing big data time series: Mining trillions of time series subsequences under dynamic time warping T Rakthanmanon, B Campana, A Mueen, G Batista, B Westover, Q Zhu, ...
ACM Transactions on Knowledge Discovery from Data (TKDD) 7 (3), 1-31, 2013
194 2013 Time series epenthesis: Clustering time series streams requires ignoring some data T Rakthanmanon, EJ Keogh, S Lonardi, S Evans
2011 IEEE 11th International Conference on Data Mining, 547-556, 2011
114 2011 Beyond one billion time series: indexing and mining very large time series collections with A Camerra, J Shieh, T Palpanas, T Rakthanmanon, E Keogh
Knowledge and information systems 39 (1), 123-151, 2014
104 2014 E-stream: Evolution-based technique for stream clustering K Udommanetanakit, T Rakthanmanon, K Waiyamai
International conference on advanced data mining and applications, 605-615, 2007
104 2007 Discovering the intrinsic cardinality and dimensionality of time series using MDL B Hu, T Rakthanmanon, Y Hao, S Evans, S Lonardi, E Keogh
2011 IEEE 11th international conference on data mining, 1086-1091, 2011
72 2011 MDL-based time series clustering T Rakthanmanon, EJ Keogh, S Lonardi, S Evans
Knowledge and information systems 33 (2), 371-399, 2012
54 2012 A novel approximation to dynamic time warping allows anytime clustering of massive time series datasets Q Zhu, G Batista, T Rakthanmanon, E Keogh
Proceedings of the 2012 SIAM international conference on data mining, 999-1010, 2012
42 2012 Efficient proper length time series motif discovery S Yingchareonthawornchai, H Sivaraks, T Rakthanmanon, ...
2013 IEEE 13th International Conference on Data Mining, 1265-1270, 2013
30 2013 Towards never-ending learning from time series streams Y Hao, Y Chen, J Zakaria, B Hu, T Rakthanmanon, E Keogh
Proceedings of the 19th ACM SIGKDD international conference on Knowledge …, 2013
26 2013 Rapid annotation of interictal epileptiform discharges via template matching under dynamic time warping J Jing, J Dauwels, T Rakthanmanon, E Keogh, SS Cash, MB Westover
Journal of neuroscience methods 274, 179-190, 2016
21 2016 Towards a minimum description length based stopping criterion for semi-supervised time series classification N Begum, B Hu, T Rakthanmanon, E Keogh
2013 IEEE 14th International Conference on Information Reuse & Integration …, 2013
21 2013 Using the minimum description length to discover the intrinsic cardinality and dimensionality of time series B Hu, T Rakthanmanon, Y Hao, S Evans, S Lonardi, E Keogh
Data Mining and Knowledge Discovery 29 (2), 358-399, 2015
18 2015 Mining historical documents for near-duplicate figures T Rakthanmanon, Q Zhu, EJ Keogh
2011 IEEE 11th International Conference on Data Mining, 557-566, 2011
15 2011 A minimum description length technique for semi-supervised time series classification N Begum, B Hu, T Rakthanmanon, E Keogh
Integration of reusable systems, 171-192, 2014
13 2014 Image mining of historical manuscripts to establish provenance B Hu, T Rakthanmanon, B Campana, A Mueen, E Keogh
Proceedings of the 2012 SIAM International Conference on Data Mining, 804-815, 2012
13 2012 A fast LSH-based similarity search method for multivariate time series C Yu, L Luo, LLH Chan, T Rakthanmanon, S Nutanong
Information Sciences 476, 337-356, 2019
12 2019 A scalable framework for cross-lingual authorship identification R Sarwar, Q Li, T Rakthanmanon, S Nutanong
Information Sciences 465, 323-339, 2018
12 2018 A general framework for never-ending learning from time series streams Y Chen, Y Hao, T Rakthanmanon, J Zakaria, B Hu, E Keogh
Data mining and knowledge discovery 29 (6), 1622-1664, 2015
11 2015 
Eamonn KeoghProfessor of Computer Science, University of California - RiversideVerified email at cs.ucr.edu
