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
694 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
280 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
147 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
100 2011 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
84 2007 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
81 2014 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
62 2011 MDL-based time series clustering T Rakthanmanon, EJ Keogh, S Lonardi, S Evans
Knowledge and information systems 33 (2), 371-399, 2012
51 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
36 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
23 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
23 2013 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
19 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
14 2016 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
14 2014 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
13 2015 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
12 2012 Mining historical documents for near-duplicate figures T Rakthanmanon, Q Zhu, EJ Keogh
2011 IEEE 11th International Conference on Data Mining, 557-566, 2011
12 2011 Data mining a trillion time series subsequences under dynamic time warping T Rakthanmanon, E Keogh
Twenty-Third International Joint Conference on Artificial Intelligence, 2013
11 2013 Object-oriented database mining: Use of object oriented concepts for improving data classification technique K Waiyamai, C Songsiri, T Rakthanmanon
International Conference on Computational Science, 303-309, 2004
10 2004 Se-stream: Dimension projection for evolution-based clustering of high dimensional data streams R Chairukwattana, T Kangkachit, T Rakthanmanon, K Waiyamai
Knowledge and Systems Engineering, 365-376, 2014
8 2014 
Eamonn KeoghProfessor of Computer Science, University of California - RiversideVerified email at cs.ucr.edu
