| 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 | 2884 | 2003 |
| 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 | 2188 | 2007 |
| 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 | 1200 | 2005 |
| 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 | 893 | 2005 |
| Rotation-invariant similarity in time series using bag-of-patterns representation J Lin, R Khade, Y Li Journal of Intelligent Information Systems 39, 287-315, 2012 | 422 | 2012 |
| Mining motifs in massive time series databases P Patel, E Keogh, J Lin, S Lonardi 2002 IEEE International Conference on Data Mining, 2002. Proceedings., 370-377, 2002 | 361 | 2002 |
| Tapnet: Multivariate time series classification with attentional prototypical network X Zhang, Y Gao, J Lin, CT Lu Proceedings of the AAAI conference on artificial intelligence 34 (04), 6845-6852, 2020 | 329 | 2020 |
| Finding the most unusual time series subsequence: algorithms and applications E Keogh, J Lin, SH Lee, HV Herle Knowledge and Information Systems 11, 1-27, 2007 | 314 | 2007 |
| Mining time series data CA Ralanamahatana, J Lin, D Gunopulos, E Keogh, M Vlachos, G Das Data mining and knowledge discovery handbook, 1069-1103, 2005 | 289 | 2005 |
| Iterative incremental clustering of time series J Lin, M Vlachos, E Keogh, D Gunopulos Advances in Database Technology-EDBT 2004: 9th International Conference on …, 2004 | 288 | 2004 |
| A wavelet-based anytime algorithm for k-means clustering of time series M Vlachos, J Lin, E Keogh, D Gunopulos Workshop on Clustering High Dimensionality Data and Its Applications, at the …, 2003 | 248 | 2003 |
| Visually mining and monitoring massive time series J Lin, E Keogh, S Lonardi, JP Lankford, DM Nystrom Proceedings of the tenth ACM SIGKDD international conference on Knowledge …, 2004 | 240 | 2004 |
| Approximations to magic: Finding unusual medical time series J Lin, E Keogh, A Fu, H Van Herle 18th IEEE Symposium on Computer-Based Medical Systems (CBMS'05), 329-334, 2005 | 209 | 2005 |
| Visualizing and discovering non-trivial patterns in large time series databases J Lin, E Keogh, S Lonardi Information visualization 4 (2), 61-82, 2005 | 194 | 2005 |
| Finding structural similarity in time series data using bag-of-patterns representation J Lin, Y Li Scientific and Statistical Database Management: 21st International …, 2009 | 190 | 2009 |
| Time series anomaly discovery with grammar-based compression. P Senin, J Lin, X Wang, T Oates, S Gandhi, AP Boedihardjo, C Chen, ... Edbt, 481-492, 2015 | 125 | 2015 |
| Grammarviz 2.0: a tool for grammar-based pattern discovery in time series P Senin, J Lin, X Wang, T Oates, S Gandhi, AP Boedihardjo, C Chen, ... Machine Learning and Knowledge Discovery in Databases: European Conference …, 2014 | 119 | 2014 |
| Finding unusual medical time-series subsequences: Algorithms and applications E Keogh, J Lin, AW Fu, H Van Herle IEEE Transactions on Information Technology in Biomedicine 10 (3), 429-439, 2006 | 115 | 2006 |
| Dimensionality reduction by random projection and latent semantic indexing J Lin, D Gunopulos proceedings of the Text Mining Workshop, at the 3rd SIAM International …, 2003 | 114 | 2003 |
| Visualizing variable-length time series motifs Y Li, J Lin, T Oates Proceedings of the 2012 SIAM international conference on data mining, 895-906, 2012 | 108 | 2012 |
Eamonn KeoghDistinguished Professor of Computer Science, University of California - RiversideVerified email at cs.ucr.edu
