| 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 | 645 | 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 | 261 | 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), 10, 2013 | 129 | 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 | 98 | 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 | 81 | 2007 |
| Beyond one billion time series: indexing and mining very large time series collections with SAX2+ A Camerra, J Shieh, T Palpanas, T Rakthanmanon, E Keogh Knowledge and information systems 39 (1), 123-151, 2014 | 73 | 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 | 59 | 2011 |
| MDL-based time series clustering T Rakthanmanon, EJ Keogh, S Lonardi, S Evans Knowledge and information systems 33 (2), 371-399, 2012 | 49 | 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 | 34 | 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 | 25 | 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 | 18 | 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 | 17 | 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 | 13 | 2015 |
| 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 | 12 | 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 | 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 |
| 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 | 11 | 2016 |
| Data mining a trillion time series subsequences under dynamic time warping T Rakthanmanon, E Keogh Twenty-Third International Joint Conference on Artificial Intelligence, 2013 | 10 | 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 |
| DCR: Discretization using class information to reduce number of intervals P Pongaksorn, T Rakthanmanon, K Waiyamai Quality issues, measures of interestingness and evaluation of data mining …, 2009 | 9 | 2009 |
Eamonn KeoghProfessor of Computer Science, University of California - RiversideVerified email at cs.ucr.edu
