| Exact indexing of dynamic time warping E Keogh, CA Ratanamahatana Knowledge and information systems 7, 358-386, 2005 | 3577 | 2005 |
| A symbolic representation of time series, with implications for streaming algorithms J Lin, E Keogh, S Lonardi, B Chiu Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining …, 2003 | 2859 | 2003 |
| Dimensionality reduction for fast similarity search in large time series databases E Keogh, K Chakrabarti, M Pazzani, S Mehrotra Knowledge and information Systems 3, 263-286, 2001 | 2234 | 2001 |
| Experiencing SAX: a novel symbolic representation of time series J Lin, E Keogh, L Wei, S Lonardi Data Mining and knowledge discovery 15, 107-144, 2007 | 2163 | 2007 |
| On the need for time series data mining benchmarks: a survey and empirical demonstration E Keogh, S Kasetty Proceedings of the eighth ACM SIGKDD international conference on Knowledge …, 2002 | 1902 | 2002 |
| The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances A Bagnall, J Lines, A Bostrom, J Large, E Keogh Data mining and knowledge discovery 31, 606-660, 2017 | 1859 | 2017 |
| Querying and mining of time series data: experimental comparison of representations and distance measures H Ding, G Trajcevski, P Scheuermann, X Wang, E Keogh Proceedings of the VLDB Endowment 1 (2), 1542-1552, 2008 | 1837 | 2008 |
| An online algorithm for segmenting time series E Keogh, S Chu, D Hart, M Pazzani Proceedings 2001 IEEE international conference on data mining, 289-296, 2001 | 1714 | 2001 |
| Time series shapelets: a new primitive for data mining L Ye, E Keogh Proceedings of the 15th ACM SIGKDD international conference on Knowledge …, 2009 | 1402 | 2009 |
| 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 | 1356 | 2012 |
| Locally adaptive dimensionality reduction for indexing large time series databases E Keogh, K Chakrabarti, M Pazzani, S Mehrotra Proceedings of the 2001 ACM SIGMOD international conference on Management of …, 2001 | 1313 | 2001 |
| Hot sax: Efficiently finding the most unusual time series subsequence E Keogh, J Lin, A Fu Fifth IEEE International Conference on Data Mining (ICDM'05), 8 pp., 2005 | 1187 | 2005 |
| Scaling up dynamic time warping for datamining applications EJ Keogh, MJ Pazzani Proceedings of the sixth ACM SIGKDD international conference on Knowledge …, 2000 | 1136 | 2000 |
| Experimental comparison of representation methods and distance measures for time series data X Wang, A Mueen, H Ding, G Trajcevski, P Scheuermann, E Keogh Data Mining and Knowledge Discovery 26, 275-309, 2013 | 1130 | 2013 |
| The UCR time series classification archive Y Chen, E Keogh, B Hu, N Begum, A Bagnall, A Mueen, G Batista July, 2015 | 1057 | 2015 |
| The UCR time series archive HA Dau, A Bagnall, K Kamgar, CCM Yeh, Y Zhu, S Gharghabi, ... IEEE/CAA Journal of Automatica Sinica 6 (6), 1293-1305, 2019 | 1005 | 2019 |
| Segmenting time series: A survey and novel approach E Keogh, S Chu, D Hart, M Pazzani Data mining in time series databases, 1-21, 2004 | 956 | 2004 |
| Clustering of time-series subsequences is meaningless: implications for previous and future research E Keogh, J Lin Knowledge and information systems 8, 154-177, 2005 | 885 | 2005 |
| Towards parameter-free data mining E Keogh, S Lonardi, CA Ratanamahatana Proceedings of the tenth ACM SIGKDD international conference on Knowledge …, 2004 | 880 | 2004 |
| 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 | 848 | 2016 |
Abdullah MueenProfessor, Department of Computer Science, University of New Mexico在 cs.unm.edu 的电子邮件经过验证
