| The UCR time series classification archive Y Chen, E Keogh, B Hu, N Begum, A Bagnall, A Mueen, G Batista July, 2015 | 1471* | 2015 |
| Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets CCM Yeh, Y Zhu, L Ulanova, N Begum, Y Ding, HA Dau, DF Silva, ... 2016 IEEE 16th international conference on data mining (ICDM), 1317-1322, 2016 | 879 | 2016 |
| The UCR time series classification archive HA Dau, E Keogh, K Kamgar, CCM Yeh, Y Zhu, S Gharghabi, ... URL https://www. cs. ucr. edu/~ eamonn/time_series_data_2018, 2018 | 304 | 2018 |
| Time series joins, motifs, discords and shapelets: a unifying view that exploits the matrix profile CCM Yeh, Y Zhu, L Ulanova, N Begum, Y Ding, HA Dau, Z Zimmerman, ... Data Mining and Knowledge Discovery 32, 83-123, 2018 | 176 | 2018 |
| Accelerating dynamic time warping clustering with a novel admissible pruning strategy N Begum, L Ulanova, J Wang, E Keogh Proceedings of the 21th ACM SIGKDD international conference on knowledge …, 2015 | 164 | 2015 |
| Hexagon-ML,“The ucr time series classification archive,” October 2018 HA Dau, E Keogh, K Kamgar, CCM Yeh, Y Zhu, S Gharghabi, ... URL https://www. cs. ucr. edu/~ eamonn/time_series_data_2018, 2018 | 113 | 2018 |
| Rare time series motif discovery from unbounded streams N Begum, E Keogh Proceedings of the VLDB Endowment 8 (2), 149-160, 2014 | 88 | 2014 |
| Scalable clustering of time series with u-shapelets L Ulanova, N Begum, E Keogh Proceedings of the 2015 SIAM international conference on data mining, 900-908, 2015 | 79 | 2015 |
| Semi-supervision dramatically improves time series clustering under dynamic time warping HA Dau, N Begum, E Keogh Proceedings of the 25th ACM International on Conference on Information and …, 2016 | 55 | 2016 |
| Hexagon-ML (2018) The UCR time series classification archive HA Dau, E Keogh, K Kamgar, CCM Yeh, Y Zhu, S Gharghabi, ... URL https://www. cs. ucr. edu/~ eamonn/time_series_data_2018, 2018 | 53 | 2018 |
| 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 | 30 | 2013 |
| 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 | 21 | 2014 |
| Clustering in the face of fast changing streams L Ulanova, N Begum, M Shokoohi-Yekta, E Keogh Proceedings of the 2016 SIAM International Conference on Data Mining, 1-9, 2016 | 15 | 2016 |
| A general framework for density based time series clustering exploiting a novel admissible pruning strategy N Begum, L Ulanova, HA Dau, J Wang, E Keogh arXiv preprint arXiv:1612.00637, 2016 | 9 | 2016 |
| Rare Pattern Discovery from Time Series N Begum, E Keogh Proceedings of the Int’l Conference on Very Large Databases (VLDB), Kohala …, 2015 | 2 | 2015 |
| Optimal queries processing in a heterogeneous sensor network using multicommodity network flow method N Begum, S Tasnim, M Naznin 2010 IEEE 17Th International Conference on Industrial Engineering and …, 2010 | 2 | 2010 |
| Exploiting Time Series Primitives to Solve Realistic Data Mining Problems N Begum University of California, Riverside, 2016 | | 2016 |
| Adhikar S.(See Dasgupta M.) Agarwal N.(See Kriplani A.) Agrawal S., Gupta T.….................... S61 Aparna R.(See Vinutha) Ariachery CA (See Kriplani A.) U Athavale, B Banerjee, S Banerjee, S Basak, A Basu, N Begum, ... The Journal of Obstetrics and Gynecology of India (November–December 2013 …, 0 | | |
| Adhikar S.(See Dasgupta M.) Agarwal N.(See Kriplani A.) Agrawal S., Gupta T.….................... S61 Aparna R.(See Vinutha) Ariachery CA (See Kriplani A.) U Athavale, B Banerjee, S Banerjee, S Basak, A Basu, N Begum, ... The Journal of Obstetrics and Gynecology of India (November–December 2013 …, 0 | | |
| DSAA 2018 Program Committee N Ahmed, H Alatrista-Salas, A Anagnostopoulos, R Assuncao, S Banerjee, ... | | |
Eamonn KeoghDistinguished Professor of Computer Science, University of California - RiversideBestätigte E-Mail-Adresse bei cs.ucr.edu