| 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 | 2276 | 2003 |
| Experiencing SAX: a novel symbolic representation of time series J Lin, E Keogh, L Wei, S Lonardi Data Mining and knowledge discovery 15 (2), 107-144, 2007 | 1573 | 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 | 897 | 2005 |
| Clustering of time-series subsequences is meaningless: implications for previous and future research E Keogh, J Lin Knowledge and information systems 8 (2), 154-177, 2005 | 728 | 2005 |
| 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 | 297 | 2002 |
| Rotation-invariant similarity in time series using bag-of-patterns representation J Lin, R Khade, Y Li Journal of Intelligent Information Systems 39 (2), 287-315, 2012 | 276 | 2012 |
| Iterative incremental clustering of time series J Lin, M Vlachos, E Keogh, D Gunopulos International Conference on Extending Database Technology, 106-122, 2004 | 245 | 2004 |
| Finding the most unusual time series subsequence: algorithms and applications E Keogh, J Lin, SH Lee, H Van Herle Knowledge and Information Systems 11 (1), 1-27, 2007 | 233 | 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 | 226 | 2005 |
| A wavelet-based anytime algorithm for k-means clustering of time series M Vlachos, J Lin, E Keogh, D Gunopulos In proc. workshop on clustering high dimensionality data and its applications, 2003 | 213 | 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 | 210 | 2004 |
| Visualizing and discovering non-trivial patterns in large time series databases J Lin, E Keogh, S Lonardi Information visualization 4 (2), 61-82, 2005 | 173 | 2005 |
| 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 | 154 | 2005 |
| Finding structural similarity in time series data using bag-of-patterns representation J Lin, Y Li International conference on scientific and statistical database management …, 2009 | 144 | 2009 |
| 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 | 105 | 2003 |
| 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 | 95 | 2006 |
| 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 | 94 | 2012 |
| Clustering of streaming time series is meaningless J Lin, E Keogh, W Truppel Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining …, 2003 | 79 | 2003 |
| Pattern recognition in time series J Lin, S Williamson, K Borne, D DeBarr Advances in Machine Learning and Data Mining for Astronomy 1 (617-645), 3, 2012 | 75 | 2012 |
| Finding time series discords based on haar transform AWC Fu, OTW Leung, E Keogh, J Lin International Conference on Advanced Data Mining and Applications, 31-41, 2006 | 74 | 2006 |
Eamonn KeoghDistinguished Professor of Computer Science, University of California - RiversideVerified email at cs.ucr.edu
