| 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 | 1237 | 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 | 525 | 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 | 298 | 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 | 158 | 2011 |
| 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 | 142 | 2014 |
| E-stream: Evolution-based technique for stream clustering K Udommanetanakit, T Rakthanmanon, K Waiyamai Advanced Data Mining and Applications: Third International Conference, ADMA …, 2007 | 131 | 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 | 77 | 2011 |
| MDL-based time series clustering T Rakthanmanon, EJ Keogh, S Lonardi, S Evans Knowledge and information systems 33, 371-399, 2012 | 70 | 2012 |
| Single channel ECG for obstructive sleep apnea severity detection using a deep learning approach N Banluesombatkul, T Rakthanmanon, T Wilaiprasitporn TENCON 2018-2018 IEEE region 10 conference, 2011-2016, 2018 | 56 | 2018 |
| 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 | 55 | 2012 |
| 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 | 42 | 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 | 40 | 2016 |
| Efficient proper length time series motif discovery S Yingchareonthawornchai, H Sivaraks, T Rakthanmanon, ... 2013 IEEE 13th International Conference on Data Mining, 1265-1270, 2013 | 40 | 2013 |
| 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, 1622-1664, 2015 | 35 | 2015 |
| 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 | 30 | 2019 |
| 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 | 28 | 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, 358-399, 2015 | 26 | 2015 |
| A scalable framework for cross-lingual authorship identification R Sarwar, Q Li, T Rakthanmanon, S Nutanong Information Sciences 465, 323-339, 2018 | 24 | 2018 |
| $ CAG $: Stylometric authorship attribution of multi-author documents using a co-authorship graph R Sarwar, N Urailertprasert, N Vannaboot, C Yu, T Rakthanmanon, ... IEEE Access 8, 18374-18393, 2020 | 22 | 2020 |
| 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 | 18 | 2014 |
Eamonn KeoghDistinguished Professor of Computer Science, University of California - RiversideE-mailová adresa ověřena na: cs.ucr.edu
