| # | Document title | Authors | Year | Source | Cited by |
| 1 | A fast LSH-based similarity search method for multivariate time series | Yu C., Luo L., Chan L.L.H., Rakthanmanon T., Rakthanmanon T., Nutanong S. | 2019 | Information Sciences
476,pp. 337-356 | 16 |
| 2 | Single Channel ECG for Obstructive Sleep Apnea Severity Detection Using a Deep Learning Approach | Banluesombatkul N., Rakthanmanon T., Rakthanmanon T., Wilaiprasitporn T. | 2019 | IEEE Region 10 Annual International Conference, Proceedings/TENCON
2018-October,pp. 2011-2016 | 13 |
| 3 | A minimum description length technique for semi-supervised time series classification | Begum N., Hu B., Rakthanmanon T., Keogh E. | 2014 | Advances in Intelligent Systems and Computing
263,pp. 171-192 | 12 |
| 4 | A general framework for never-ending learning from time series streams | Chen Y., Hao Y., Rakthanmanon T., Zakaria J., Hu B., Keogh E. | 2015 | Data Mining and Knowledge Discovery
29(6),pp. 1622-1664 | 12 |
| 5 | A scalable framework for cross-lingual authorship identification | Sarwar R., Li Q., Rakthanmanon T., Rakthanmanon T., Nutanong S. | 2018 | Information Sciences
465,pp. 323-339 | 10 |
| 6 | Using the minimum description length to discover the intrinsic cardinality and dimensionality of time series | Hu B., Rakthanmanon T., Hao Y., Evans S., Lonardi S., Keogh E. | 2015 | Data Mining and Knowledge Discovery
29(2),pp. 358-399 | 9 |
| 7 | A scalable framework for stylometric analysis of multi-author documents | Sarwar R., Yu C., Nutanong S., Urailertprasert N., Vannaboot N., Rakthanmanon T., Rakthanmanon T. | 2018 | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
10827 LNCS,pp. 813-829 | 8 |
| 8 | Image mining of historical manuscripts to establish provenance | Hu B., Rakthanmanon T., Campana B., Mueen A., Keogh E. | 2012 | Proceedings of the 12th SIAM International Conference on Data Mining, SDM 2012
,pp. 804-815 | 8 |
| 9 | Mining historical documents for near-duplicate figures | Rakthanmanon T., Zhu Q., Keogh E. | 2011 | Proceedings - IEEE International Conference on Data Mining, ICDM
,pp. 557-566 | 8 |
| 10 | CAG : Stylometric Authorship Attribution of Multi-Author Documents Using a Co-Authorship Graph | Sarwar R., Urailertprasert N., Vannaboot N., Yu C., Rakthanmanon T., Chuangsuwanich E., Nutanong S. | 2020 | IEEE Access
8,pp. 18374-18393 | 7 |
| 11 | An effective and scalable framework for authorship attribution query processing | Sarwar R., Yu C., Tungare N., Tungare N., Chitavisutthivong K., Sriratanawilai S., Xu Y., Chow D., Rakthanmanon T., Rakthanmanon T., Nutanong S. | 2018 | IEEE Access
6,pp. 50030-50048 | 6 |
| 12 | Object-oriented database mining: use of object oriented concepts for improving data classification technique | Waiyamai K., Songsiri C., Rakthanmanon T. | 2004 | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
3036,pp. 303-309 | 6 |
| 13 | Establishing the provenance of historical manuscripts with a novel distance measure | Hu B., Rakthanmanon T., Campana B.J.L., Mueen A., Keogh E. | 2015 | Pattern Analysis and Applications
18(2),pp. 313-331 | 6 |
| 14 | Efficiently finding near duplicate figures in archives of historical documents | Rakthanmanon T., Zhu Q., Keogh E. | 2012 | Journal of Multimedia
7(2),pp. 109-123 | 4 |
| 15 | SE-Stream: Dimension projection for evolution-based clustering of high dimensional data streams | Chairukwattana R., Kangkachit T., Rakthanmanon T., Waiyamai K. | 2014 | Advances in Intelligent Systems and Computing
245,pp. 365-376 | 4 |
| 16 | SED-Stream: Discriminative dimension selection for evolution-based clustering of high dimensional data streams | Waiyamai K., Kangkachit T., Rakthanmanon T., Chairukwattana R. | 2014 | International Journal of Intelligent Systems Technologies and Applications
13(3),pp. 187-201 | 3 |
| 17 | Towards discovering the intrinsic cardinality and dimensionality of time series using MDL | Hu B., Rakthanmanon T., Hao Y., Evans S., Lonardi S., Keogh E. | 2013 | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
7070 LNAI,pp. 184-197 | 3 |
| 18 | Efficient evolution-based clustering of high dimensional data streams with dimension projection | Chairukwattana R., Kangkachit T., Rakthanmanon T., Waiyamai K. | 2013 | 2013 International Computer Science and Engineering Conference, ICSEC 2013
,pp. 185-190 | 3 |
| 19 | Clustering of symbols using minimal description length | Tataw O.M., Rakthanmanon T., Keogh E.J. | 2013 | Proceedings of the International Conference on Document Analysis and Recognition, ICDAR
,pp. 180-184 | 3 |
| 20 | AC-Stream: Associative classification over data streams using multiple class association rules | Saengthongloun B., Kangkachit T., Rakthanmanon T., Waiyamai K. | 2013 | Proceedings of the 2013 10th International Joint Conference on Computer Science and Software Engineering, JCSSE 2013
,pp. 223-228 | 3 |
| 21 | Prediction of enzyme class by using reactive motifs generated from binding and catalytic sites | Liewlom P., Rakthanmanon T., Waiyamai K. | 2007 | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
4632 LNAI,pp. 442-453 | 3 |
| 22 | Concept lattice-based mutation control for reactive motifs discovery | Waiyamai K., Liewlom P., Kangkachit T., Rakthanmanon T. | 2008 | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
5012 LNAI,pp. 767-776 | 3 |
| 23 | StyloThai: A scalable framework for stylometric authorship identification of Thai documents | Sarwar R., Porthaveepong T., Rutherford A., Rakthanmanon T., Rakthanmanon T., Nutanong S. | 2020 | ACM Transactions on Asian and Low-Resource Language Information Processing
19(3) | 3 |
| 24 | Native Language Identification of Fluent and Advanced Non-Native Writers | Sarwar R., Rutherford A.T., Hassan S.U., Rakthanmanon T., Nutanong S. | 2020 | ACM Transactions on Asian and Low-Resource Language Information Processing
19(4) | 2 |
| 25 | Semi-supervised stream clustering using labeled data points | Treechalong K., Rakthanmanon T., Waiyamai K. | 2015 | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
9166,pp. 281-295 | 2 |
| 26 | ACCD: Associative classification over concept-drifting data streams | Waiyamai K., Kangkachit T., Saengthongloun B., Rakthanmanon T. | 2014 | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
8556 LNAI,pp. 78-90 | 1 |
| 27 | Using rule order difference criterion to decide whether to update class association rules | Kongubol K., Rakthanmanon T., Waiyamai K. | 2010 | Studies in Computational Intelligence
283,pp. 241-252 | 1 |
| 28 | Searching historical manuscripts for near-duplicate figures | Rakthanmanon T., Zhu Q., Keogh E. | 2011 | ACM International Conference Proceeding Series
,pp. 14-21 | 1 |
| 29 | Object-oriened Data Mining System: A Tightly-coupled Association Rule Discovery from Object-oriented Databases | Rakthanmanon T., Songsiri C., Waiyamai K. | 2003 | Proceedings of 41st Kasetsart University Annual Conference
,pp. 296-306 | 0 |
| 30 | Classification model with subspace data-dependent balls | Klakhaeng N., Kangkachit T., Rakthanmanon T., Waiyamai K. | 2013 | Proceedings of the 2013 10th International Joint Conference on Computer Science and Software Engineering, JCSSE 2013
,pp. 211-216 | 0 |
| 31 | AutoShapelet: Reconstructable Time Series Shapelets | Ajchariyasakchai P., Rakthanmanon T. | 2020 | 2020 24th International Computer Science and Engineering Conference, ICSEC 2020
| 0 |
| 32 | Entropy-based attribute clustering | Khomprasert A., Rakthamanon T., Waiyamai K. | 2019 | ECTI DAMT-NCON 2019 - 4th International Conference on Digital Arts, Media and Technology and 2nd ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering
,pp. 230-233 | 0 |
| 33 | Evolution and affinity-propagation based approach for data stream clustering | Sunmood A., Rakthanmanon T., Rakthanmanon T., Waiyamai K. | 2018 | ACM International Conference Proceeding Series
,pp. 97-101 | 0 |
| 34 | Information gain Aggregation-based Approach for Time Series Shapelets Discovery | Kramakum C., Rakthanmanon T., Rakthanmanon T., Waiyamai K. | 2018 | Proceedings of 2018 10th International Conference on Knowledge and Systems Engineering, KSE 2018
,pp. 97-101 | 0 |
| 35 | Hierarchical multi-label associative classification for protein function prediction using gene ontology | Sangsuriyun S., Rakthanmanon T., Waiyamai K. | 2019 | Chiang Mai Journal of Science
46(1),pp. 165-179 | 0 |
| 36 | Searching and mining trillions of time series subsequences under dynamic time warping | Rakthanmanon T., Campana B., Mueen A., Batista G., Westover B., Zhu Q., Zakaria J., Keogh E. | 2012 | Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
,pp. 262-270 | 610 |
| 37 | Fast shapelets: A scalable algorithm for discovering time series shapelets | Rakthanmanon T., Keogh E. | 2013 | Proceedings of the 2013 SIAM International Conference on Data Mining, SDM 2013
,pp. 668-676 | 245 |
| 38 | Addressing big data time series: Mining trillions of time series subsequences under dynamic time warping | Rakthanmanon T., Campana B., Mueen A., Batista G., Westover B., Zhu Q., Zakaria J., Keogh E. | 2013 | ACM Transactions on Knowledge Discovery from Data
7(3) | 129 |
| 39 | Time series epenthesis: Clustering time series streams requires ignoring some data | Rakthanmanon T., Keogh E., Lonardi S., Evans S. | 2011 | Proceedings - IEEE International Conference on Data Mining, ICDM
,pp. 547-556 | 76 |
| 40 | Beyond one billion time series: Indexing and mining very large time series collections with iSAX2+ | Camerra A., Shieh J., Palpanas T., Rakthanmanon T., Keogh E. | 2014 | Knowledge and Information Systems
39(1),pp. 123-151 | 72 |
| 41 | E-stream: Evolution-based technique for stream clustering | Udommanetanakit K., Rakthanmanon T., Waiyamai K. | 2007 | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
4632 LNAI,pp. 605-615 | 65 |
| 42 | MDL-based time series clustering | Rakthanmanon T., Keogh E., Lonardi S., Evans S. | 2012 | Knowledge and Information Systems
33(2),pp. 371-399 | 40 |
| 43 | Discovering the intrinsic cardinality and dimensionality of time series using MDL | Hu B., Rakthanmanon T., Hao Y., Evans S., Lonardi S., Keogh E. | 2011 | Proceedings - IEEE International Conference on Data Mining, ICDM
,pp. 1086-1091 | 40 |
| 44 | A novel approximation to dynamic time warping allows anytime clustering of massive time series datasets | Zhu Q., Batista G., Rakthanmanon T., Keogh E. | 2012 | Proceedings of the 12th SIAM International Conference on Data Mining, SDM 2012
,pp. 999-1010 | 28 |
| 45 | Efficient proper length time series motif discovery | Yingchareonthawornchai S., Sivaraks H., Rakthanmanon T., Ratanamahatana C. | 2013 | Proceedings - IEEE International Conference on Data Mining, ICDM
,pp. 1265-1270 | 23 |
| 46 | Rapid annotation of interictal epileptiform discharges via template matching under Dynamic Time Warping | Jing J., Dauwels J., Rakthanmanon T., Keogh E., Cash S., Westover M. | 2016 | Journal of Neuroscience Methods
274,pp. 179-190 | 18 |
| 47 | Towards never-ending learning from time series streams | Hao Y., Chen Y., Zakaria J., Hu B., Rakthanmanon T., Keogh E. | 2013 | Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Part F128815,pp. 874-882 | 17 |
| 48 | Data mining a trillion time series subsequences under dynamic time warping | Rakthanmanon T., Campana B., Mueen A., Batista G., Westover B., Zhu Q., Zakaria J., Keogh E. | 2013 | IJCAI International Joint Conference on Artificial Intelligence
,pp. 3047-3051 | 16 |
| 49 | Towards a minimum description length based stopping criterion for semi-supervised time series classification | Begum N., Hu B., Rakthanmanon T., Keogh E. | 2013 | Proceedings of the 2013 IEEE 14th International Conference on Information Reuse and Integration, IEEE IRI 2013
,pp. 333-340 | 16 |