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Thanawin Rakthanmanon
Thanawin Rakthanmanon
Dept. of Computer Engineering, Kasetsart University, Thailand
Verified email at ku.ac.th
Title
Cited by
Cited by
Year
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
10102012
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
4332013
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
2482013
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
1372011
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
1212014
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
1192007
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
742011
MDL-based time series clustering
T Rakthanmanon, EJ Keogh, S Lonardi, S Evans
Knowledge and information systems 33 (2), 371-399, 2012
622012
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
482012
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
342013
Efficient proper length time series motif discovery
S Yingchareonthawornchai, H Sivaraks, T Rakthanmanon, ...
2013 IEEE 13th International Conference on Data Mining, 1265-1270, 2013
332013
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
272016
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
272013
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
222015
A scalable framework for cross-lingual authorship identification
R Sarwar, Q Li, T Rakthanmanon, S Nutanong
Information Sciences 465, 323-339, 2018
212018
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
202019
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
202015
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
172014
Data Mining a Trillion Time Series Subsequences Under Dynamic Time Warping.
T Rakthanmanon, EJ Keogh
IJCAI, 3047-3051, 2013
172013
Mining historical documents for near-duplicate figures
T Rakthanmanon, Q Zhu, EJ Keogh
2011 IEEE 11th International Conference on Data Mining, 557-566, 2011
162011
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