Selected Publications

• Prommy Sultana Hossain, Emanuela Marasco, Jessica Lin, and Michael King. 2025. I Forgot About You!: Exploring Multi-Label Unlearning (MLU) for Responsible Facial Systems. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD 2025). Porto, Portugal. September 15-19. [pdf]
• Wenjie Xi and Jessica Lin. 2025. RandomAD: Time Series Anomaly Detection with Untrained Convolutional Kernels. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD 2025). Porto, Portugal. September 15-19. [pdf]
• Xiaosheng Li, Wenjie Xi, and Jessica Lin. 2024. RandomNet: Clustering Time Series Using Untrained Deep Neural Networks. Data Min Knowl Disc 38, 3473–3502. [Code]
• Wenjie Xi, Arnav Jain, Li Zhang and Jessica Lin. 2024. Efficient and Accurate Similarity-Aware Graph Neural Network for Semi-Supervised Time Series Classification. In Proceedings of the 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2024). Taipei, Taiwan. May 7-10. [Code]
• Li Zhang, Jiahao Ding, Yifeng Gao, and Jessica Lin. 2023. PMP: Privacy-Aware Matrix Profile against Sensitive Pattern Inference for Time Series. In Proceedings of the 2023 SIAM International Conference on Data Mining (SDM 2023). Minneapolis, MN. April 27-29. [pdf]
• Wenjie Xi, Arnav Jain, Li Zhang and Jessica Lin. 2023. LB-SimTSC: An Efficient Similarity-Aware Graph Neural Network for Semi-Supervised Time Series Classification. The Ninth International Workshop on Deep Learning on Graphs: Method and Applications, in conjunction with AAAI 2023. Washington DC. Feb 7-14.
• Li Zhang, Nital Patal, Xiuqi Li, and Jessica Lin. 2022. Joint Time Series Chain: Detecting Unusual Evolving Trend across Time Series, In Proceedings of the 2022 SIAM International Conference on Data Mining (SDM 2022). Alexandria, VA. April 28-30. [pdf] [Code]
• Li Zhang, Yan Zhu, Yifeng Gao, and Jessica Lin. 2022. Robust Time Series Chain Discovery with Incremental Nearest Neighbors. In Proceedings of the 2022 IEEE International Conference on Data Mining (ICDM 2022). Orlando, FL. Nov 28-Dec 1. [Extended version] [Website]
• Qingzhe Li, Liang Zhao, Yi-Ching Lee, Avesta Sassan, and Jessica Lin. 2021. CPM: A general feature dependency pattern mining framework for contrast multivariate time series. Pattern Recognition. Vol 12.
• Xiaosheng Li, Jessica Lin, and Liang Zhao. 2021. Time Series Clustering in Linear Time Complexity. Data Min Knowl Disc, 35(6): 2369-2388. [Code]
• Sayadi, Hossein, Yifeng Gao, Hosein Mohammadi Makrani, Jessica Lin, Paulo Cesar Costa, Setareh Rafatirad, and Houman Homayoun. 2021. Towards Accurate Run-Time Hardware-Assisted Stealthy Malware Detection: A Lightweight, Yet Effective Time Series CNN-Based Approach. Cryptography 5, no. 4: 28.
• Yifeng Gao, Hosein Mohammadi Makrani, Mehrdad Aliasgari, Amin Rezaei, Jessica Lin, Houman Homayoun, and Hossein Sayadi. 2021. Adaptive-HMD: Accurate and Cost-Efficient Machine Learning-Driven Malware Detection using Microarchitectural Events. In Proceedings of the 2021 IEEE 27th International Symposium on On-Line Testing and Robust System Design (IOLTS 2021). Torino, Italy. June 28-30.
• Qingzhe Li, Liang Zhao, Yi-Ching Lee and Jessica Lin. 2020. Contrast Pattern Mining in Paired Multivariate Time Series of a Controlled Driving Behavior Experiment. ACM Transactions on Spatial Algorithms and Systems (TSAS). 6(4):1-28.
• Li Zhang, Yifeng Gao, and Jessica Lin. 2020. Semantic Discord: Finding Unusual Local Patterns for Time Series. In SIAM International Conference on Data Mining (SDM 2020), Cincinnati, May 2020. [pdf] [Code]
• Xuchao Zhang, Yifeng Gao, Jessica Lin, and Chang-Tien Lu. 2020. TapNet: Multivariate Time Series Classification with Attentional Prototype Network. In Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI 2020), New York, Feb. 2020. [pdf] [Full] *Full version contains the experiment results with all 30 UEA datasets.
• H. Sayadi, Y. Gao, H. Makrani, A. Sasan, J. Lin, S. Rafatirad, and H. Homayoun. 2020. Towards Runtime Hardware-Assisted Stealthy Malware Detection. In Proceedings of the 45th Government Microcircuit Applications and Critical Technology Conference (GOMACTech’20), March 2020.
• Yifeng Gao, Jessica Lin, and Constantin Brif. 2020. Ensemble Grammar Induction for Detecting Anomalies in Time Series. In Proceedings of the 23rd International Conference on Extending Database Technology (EDBT 2020), Copenhagen, Mar. 2020.
• Hossein Sayadi, Yifeng Gao, Hosein Mohammadi Makrani, Sai Manoj, Avesta Sasan, Setareh Rafatirad, Jessica Lin, and Houman Homayoun. 2020. StealthMiner: Specialized Time Series Machine Learning for Run-Time Stealthy Malware Detection based on Microarchitectural Features. Great Lakes Symposium on VLSI (GLSVLSI ’20). Association for Computing Machinery, New York, NY. Sep 8-11.
• Yifeng Gao and Jessica Lin. 2019. HIME: Discovering Variable-length Motifs in Large-Scale Time Series. Knowledge and Information Systems Journal (KAIS), Springer, 2019, 61(1), pp.513-542.
• Xiaosheng Li and Jessica Lin. 2019. Linear Time Motif Discovery in Time Series. In Proceedings of the 2019 SIAM International Conference on Data Mining (SDM). Calgary, Alberta, Canada. [Code]
• Iqbal Owadally, Feng Zhou, Rasaq Otunba, Jessica Lin, Douglas Wright. 2019. An agent-based system with temporal data mining for monitoring financial stability on insurance markets. Expert Syst. Appl. 123: 270-282.
• Rasaq Otunba, Raimi A. Rufai, and Jessica Lin. 2019. Deep Stacked Ensemble Recommender. In Proceedings of the 31st International Conference on Scientific and Statistical Database Management (SSDBM). Santa Cruz, CA. July 23-25, 2019.
• Rohan Khade, Jessica Lin, Nital Patel. 2019. Finding Meaningful Contrast Patterns for Quantitative Data. In Proceedings of the 22nd International Conference on Extending Database Technology (EDBT 2019): 444-455.
• Yifeng Gao and Jessica Lin. 2019. Discovering Subdimensional Motifs of Different Lengths in Large-Scale Multivariate Time Series. In Proceedings of the 19th IEEE International Conference on Data Mining (ICDM 2019). Beijing, Nov. 2019. [Code]
• Qingzhe Li, Liang Zhao, Yi-Ching Lee, Yanfang Ye, Jessica Lin, and Lingfei Wu. 2019. Contrast Feature Dependency Pattern Mining for Controlled Experiments with Application to Driving Behavior. In Proceedings of the 19th IEEE International Conference on Data Mining (ICDM 2019), short paper. Beijing, Nov. 2019.
• Xiaosheng Li, Jessica Lin, and Liang Zhao. 2019. Linear Time Complexity Time Series Clustering with Symbolic Pattern Forest. In Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI 2019). Aug 10-16. Macao, China.
• Yifeng Gao and Jessica Lin. 2018. Exploring variable-length time series motifs in one hundred million length scale. Data Min. Knowl. Discov. 32(5): 1200-1228.
• Xing Wang, Jessica Lin, Nital Patel, and Martin Braun. 2018. Exact variable-length anomaly detection algorithm for univariate and multivariate time series. Data Min. Knowl. Discov. 32(6): 1806-1844
• Pavel Senin, Jessica Lin, Xing Wang, Tim Oates, Sunil Gandhi, Arnold P. Boedihardjo, Crystal Chen, Susan Frankenstein. 2018. GrammarViz 3.0: Interactive Discovery of Variable-Length Time Series Patterns. TKDD 12(1): 10:1-10:28.
• Xiaosheng Li and Jessica Lin. 2018. Evolving Separating References for Time Series Classication. In Proceedings of the SIAM International Conference in Data Mining (SDM). San Diego, CA. May 3-5. [pdf] [Code]
• Daoyuan Li, Jessica Lin, Tegawende Bissyande, Jacques Klein, and Yves LeTraon. 2018. Extracting Statistical Graph Features for Accurate and Efficient Time Series Classification. In Proceedings of the 21st International Conference on Extending Database Technology (EDBT). Vienna, Austria. Mar 26-29.
• Rohan Khade, Jessica Lin, Nital Patel and Martin Braun. 2018. Finding Contrast Patterns for Mixed Streaming Data. In Proceedings of the 21st International Conference on Extending Database Technology (EDBT). Vienna, Austria. Mar 26-29.
• Yifeng Gao and Jessica Lin. 2017. Efficient Discovery of Time Series Motifs with Large Length Range in Million Scale Time Series. In Proceedings of The 2017 IEEE International Conference on Data Mining series (ICDM). New Orleans, LA. Nov 18-21.  [Website]
• Xiaosheng Li and Jessica Lin. 2017. Linear Time Complexity Time Series Classification with Bag-of-Pattern-Features. In Proceedings of The 2017 IEEE International Conference on Data Mining series (ICDM). New Orleans, LA. Nov 18-21. [Website] [Code]
• Qingzhe Li, Jessica Lin, Liang Zhao, and Huzefa Rangwala. 2017. A Uniform Representation for Trajectory Learning Tasks. In Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2017). Redondo Beach, CA. Nov 7-10.
• Yifeng Gao, Qingzhe Li, Xiaosheng Li, Jessica Lin, and Huzefa Rangwala. 2017. TrajViz: A Tool for Visualizing Patterns and Anomalies in Trajectory In Proceedings of the European Conference on Machine Learning & Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD). Skopje, Macedonia. Sep 18-22. [Code]
• Crystal Chen, Arnold P. Boedihardjo, Brian S. Jenkins, Charlotte L. Ellison, Jessica Lin, Pavel Senin, and Tim Oates. 2017. STAVIS 2.0: Mining Spatial Trajectories via Motifs. In Proceedings of the 2017 International Symposium on Spatial and Temporal Databases (SSTD). Arlington, VA. Aug 21.
• Rasaq Otunba, Raimi A. Rufai, and Jessica Lin. 2017. MPR: Multi-Objective Pairwise Ranking. In Proceedings of the 11th ACM Conference on Recommender Systems (RecSys 2017). Como, Italy. Aug 27-31.
• Xing Wang, Jessica Lin, Nital Patel and Martin Braun. 2016. A Self-learning and Online Algorithm for Time Series Anomaly Detection, with Application in CPU Manufacturing. In Proceedings of the 25th ACM International Conference on Information and Knowledge Management (CIKM 2016). Indianapolis, IN. Oct 24-26.
• Gene Shuman, Zoran Duric, Daniel Barbara, Jessica Lin and Lynn H. Gerber. 2016. Improving the recognition of grips and movements of the hand using myoelectric signals. BMC Med. Inf. & Decision Making 16(S-2): 78.
• Xing Wang, Jessica Lin, Pavel Senin, Tim Oates, Sunil Gandhi, Arnold P. Boedihardjo, Crystal Chen, and Susan Frankenstein. 2016. RPM: Representative Pattern Mining for Efficient Time Series Classification. In Proceedings of the 19th International Conference on Extending Database Technology (EDBT). Bordeaux, France. March 15-18. [Website]
• Yifeng Gao, Jessica Lin, Huzefa Rangwala. 2016. Iterative Grammar-Based Framework for Discovering Variable-Length Time Series Motifs. In Proceedings of the 15th International Conference on Machine Learning and Applications (ICMLA). Anaheim, CA. December 18-20. Pages 7-12.
• Rohan Khade, Jessica Lin, and Nital S. Patel. 2015. Frequent Set Mining for Streaming Mixed and Large Data. In Proceedings of the 14th International Conference on Machine Learning and Applications (ICMLA). Miami, FL. Dec 9-11.
• Xing Wang, Yifeng Gao, Jessica Lin, Huzefa Rangwala, and Ranjeev Mittu. 2015. A Machine Learning Approach to False Alarm Detection for Critical Arrhythmia Alarms. In Proceedings of the 14th International Conference on Machine Learning and Applications (ICMLA). Miami, FL. Dec 9-11.
• Gene Shuman, Zoran Duric, Daniel Barbara, Jessica Lin, and Lynn H. Gerber. 2015. Using Myoelectric Signals to Recognize Grips and Movements of the Hand. In Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine (BIBM). Washington DC. Nov 9-12.
• Pavel Senin, Jessica Lin, Xing Wang, Tim Oates, Sunil Gandhi, Arnold P. Boedihardjo, Crystal Chen, and Susan Frankenstein. 2015. Time Series Anomaly Discovery with Grammar-Based Compressions. In Proceedings of the 18th International Conference on Extending Database Technology (EDBT). Brussels, Belgium. March 23-27.
• Pavel Senin, Jessica Lin, Xing Wang, Tim Oates, Sunil Gandhi, Arnold P. Boedihardjo, Crystal Chen, Susan Frankenstein, and Manfred Lerner. 2014. GrammarViz 2.0: a Tool for Grammar-Based Pattern Discovery in Time Series. In Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD). Nancy, France. Sep 15-19.

  [Website and source code: http://grammarviz2.github.io/grammarviz2_site/]

• Pavel Senin, Jessica Lin, Xing Wang, Tim Oates, Sunil Gandhi, Arnold P. Boedihardjo, Crystal Chen, and Susan Frankenstein. 2014. Grammar-Driven Anomaly Detection in Time Series. CSDL Technical Report 14-05.
• Uday Kamath, Jessica Lin, and Kenneth De Jong. 2014. SAX-EFG:An Evolutionary Feature Generation Framework for Time Series Classification. In Proceedings of Genetic and Evolutionary Computation Conference (GECCO '14) . Vancouver, Canada. July 12-16, 2014.
• Rasaq Otunba and Jessica Lin. 2014. APT: Approximate Period Detection in Time Series. In Proceedings of the 26th International Conference on Software Engineering and Knowledge Engineering (SEKE). Vancouver, Canada, July 1-3.
• Rasaq Otunba, Jessica Lin, and Pavel Senin. 2014. MBPD: Motif-Based Period Detection. In Proceedings of the 1st International Workshop on Pattern Mining and Application of Big Data, in conjunction with PAKDD 2014. . Tainan, Taiwan. May 13, 2014.
• Guido Cervone, Jessica Lin, and Nigel Waters (Eds.). 2013. Spatio-Temporal Data Mining for Geoinformatics: Methods and Applications. Springer-Verlag. On Amazon
• Tim Oates, Arnold Boedihardjo, Jessica Lin, Crystal Chen, Susan Frankenstein, and Sunil Gandhi. 2013. Motif discovery in spatial trajectories using grammar inference. In Proceedings of ACM International Conference on Information and Knowledge Management (CIKM). San Francisco, CA. Oct 27-Nov 1.
• Chun-Kit Ngan, Alexander Brodsky, and Jessica Lin. 2013. Multi-event decision making over multivariate time series. International Journal of Information and Decision Sciences. 5(3).
• Yuan Li, Jessica Lin, and Tim Oates. 2012. Visualizing variable-length time series motifs. In Proceedings of the 2012 SIAM International Conference on Data Mining. Anaheim, CA. Apr 26-28. Pages 895-906.
  [Website]  [Citations]

• Jessica Lin, Rohan Khade, and Yuan Li. 2012. Rotation-invariant similarity in time series using Bag-of-Patterns representation. Journal of Intelligent Information Systems. Vol 39, Issue 2. Pages 287-315.

• Chun-Kit Ngan, Alexander Brodsky, and Jessica Lin. 2012. An event-based service framework for learning, querying and monitoring multivariate time series. Lecture Notes in Business Information Processing Series, Vol 0102.
• Chun-Kit Ngan, Alexander Brodsky, and Jessica Lin. 2012. Multi-event decision making over multivariate time series. International Journal of Information and Decision Sciences.
• Chun-Kit Ngan, Alexander Brodsky, and Jessica Lin. 2012. R-Checkpoint algorithm for multi-event decision making over multivariate time series. In Proceedings of the 16th International Conference on Decision Support Systems.
• Jessica Lin, Sheri Williamson, Kirk Borne, and David DeBarr. 2012. Pattern recognition in time series. Advances in Machine Learning and Data Mining for Astronomy. Eds. Kamal, A., Srivastava, A., Way, M., and Scargle, J. Chapman & Hall.

• Chun-Kit Ngan, Alexander Brodsky, and Jessica Lin. 2011. A service framework for learning, querying, and monitoring multivariate time series. In Proceedings of the 13th International Conference on Enterprise Information Systems. Pages 92-101.
  • Best Student Paper Award
• Chun-Kit Ngan, Alexander Brodsky, and Jessica Lin. 2011. Multi-event decision making over multivariate time series. In Proceedings of EWG-DSS London 2011 Workshop on Decision Systems. Birbeck, UK. June 23-34.
• Guido Cervone, Jessica Lin, Pasquale Franzese. 2011. Addressing wind direction uncertainty in source estimation through dynamic time warping, In Proceedings of the 91st American Meteorological Society Annual Meeting, Computational Intelligence Methods and Their Applications to Environmental Science, Seattle, WA, January 2011.
• Jessica Lin, Guido Cervone, and Nigel Waters. 2011. DMGI 2010 workshop report: The First ACM SIGSPATIAL International Workshop on Data Mining for Geoinformatics. SIGSPATIAL Special 3(1): 6-7.
• Chun-Kit Ngan, Alexander Brodsky, and Jessica Lin. 2011. An event-based service framework for learning, querying, and monitoring multivariate time series. Lecture Notes in Business Information Processing. Springer-Verlag. To Appear.
• Jessica Lin, Guido Cervone, and Nigel Waters (Eds.). 2010. Proceedings of the 1st ACM SIGSPATIAL International Workshop on Data Mining for Geoinformatics. ACM, New York, NY, USA.
• Jessica Lin and Yuan Li. 2010. Finding approximate frequent patterns in streaming medical data. In Proceedings of the 23rd IEEE International Symposium on Computer-Based Medical Systems. IEEE Computer Society, Washington DC, USA.
• Chun-Kit Ngan, Alexander Brodsky, and Jessica Lin. 2010. Decisions on multivariate time series: combining domain knowledge with utility maximization. In Supplemental Proceedings of the 15th International Conference on Decision Support Systems. July 7-10. Lisbon, Portugal.
• Jessica Lin, Guido Cervone, and Pasquale Franzese. 2010. Assessment in error in air quality models using dynamic time warping. In Proceedings of the 1st International Workshop on Data Mining for Geoinformatics, in conjunction with SIGSPATIAL GIS 2010. San Jose, CA. Nov 2, 2010. Pages 38-44.
• Yuan Li and Jessica Lin. 2010. Approximate variable-length time series motif discovery using grammar inference. In Proceedings of the 10th International Workshop on Multimedia Data Mining, in conjunction with SIGKDD 2010. ACM, New York, NY, USA. Pages 1-9.
• Chotirat Ann Ratanamahatana, Jessica Lin, Dimitrios Gunopulos, Eamonn Keogh, Michail Vlachos, and Gautam Das. 2010. Mining time series data. Data Mining and Knowledge Discovery Handbook 2010, 2nd Edition. Eds. Oded Maimon, Lior Rokach. Springer. Pages 1049-1077
• Jessica Lin and Yuan Li. 2009. Finding structural similarity in time series data using Bag-of-Patterns representation. In Proceedings of the 21st International Conference on Scientific and Statistical Database Management (SSDBM 2009), Marianne Winslett (Ed.). Springer-Verlag, Berlin, Heidelberg, Pages 461-477.
• Jessica Lin and Yuan Li. 2009. Finding structurally different medical data. In Proceedings of the 22nd IEEE International Symposium on Computer-Based Medical Systems. IEEE Computer Society, Washington DC, USA. Pages 1-8.
• Jessica Lin , David Etter and Dave DeBarr. 2008. Exact and approximate reverse nearest neighbor search in multimedia data. In Proceedings of the SIAM International Conference on Data Mining. Pages 656-667.

• Eiman Al-Shammari, Jessica Lin. 2008. A novel Arabic lemmatization algorithm. In Proceedings of the 2nd SIGIR Workshop on Analytics for Noisy Unstructured Text Data. Singapore, July 24-27, 2008. p. 113-118.
• Jessica Lin, Eamonn Keogh, Li Wei, and Stefano Lonardi. 2007. Experiencing SAX: a novel symbolic representation of time series. Data Mining and Knowledge Discovery, 15(2): 107-144.
  [Citations]
• Stefano Lonardi, Jessica Lin, Eamonn Keogh, and Bill Chiu. 2007. Efficient discovery of unusual patterns in time series. New Generation Computing, 25(1): 61-93.
• David DeBarr and Jessica Lin. 2007. Time series classification challenge experiments. In Proceedings of the Workshop and Challenge on Time Series Classification, at the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Jose, CA. Aug 12-15.
• Jessica Lin, Michail Vlachos, Eamonn Keogh, and Dimitrios Gunopulos. 2007. Multi-resolution time series clustering and application to images. Multimedia Data Mining and Knowledge Discovery, Eds. Valery Al Petrushin and Latifur Khan. Springer. Pages 58-79.
• Eamonn Keogh, Jessica Lin, Sang-Hee Lee, and Helga Van Herle. 2006. Finding the most unusual time series subsequence: algorithms and applications. Knowledge and Information Systems, 11(1): 1-27.
  [Citations]
• Eamonn Keogh, Jessica Lin, Ada Fu & Helga Van Herie. 2006. Finding unusual medical time series subsequences: algorithms and applications. IEEE Transactions on Information Technology in Biomedicine, 10(3): 429-439.
• Jessica Lin and Eamonn Keogh. 2006. Group SAX: Extending the notion of contrast sets to time series and multimedia data. In Proceedings of the 10th European Conference on Principles and Practice of Knowledge Discovery in Databases. Berlin, Germany. Sept 18-22. Pages 284-296. Lecture Notes in Computer Science, Springer.
• Ada Fu, Oscar Leung, Eamonn Keogh, and Jessica Lin. 2006. Finding time series discords based on Haar transform. In Proceedings of the 2nd International Conference on Advanced Data Mining and Applications. Xi’an, China. Aug 14-18. Pages 31-41.
  [Citations]
• Jessica Lin, Eamonn Keogh, and Stefano Lonardi. 2005. Visualizing and discovering non-trivial patterns in large time series databases. Information Visualization, 4(2): 61-82.
  [Citations]
• Eamonn Keogh and Jessica Lin. 2005. Clustering of time series subsequences is meaningless: implications for previous and future research. Knowledge and Information Systems, 8(2): 154-177.
  [Citations]
• Eamonn Keogh, Jessica Lin, and Ada Fu. 2005. HOT SAX: Efficiently finding the most unusual time series subsequence. In Proceedings of the 5th IEEE International Conference on Data Mining (ICDM). Nov 27-30. Houston, TX. Pages 226-233. IEEE Computer Society.
  [Citations]
• Jessica Lin, Eamonn Keogh, Ada Fu, and Helga Van Herie. 2005. Approximations to magic: finding unusual medical time series. In Proceedings of the 18th International Symposium on Computer-Based Medical Systems. IEEE Computer Society, Washington DC, USA. Pages 329-334.
  [Citations]
• Chotirat Ann Ratanamahatana, Jessica Lin, Dimitrios Gunopulos, Eamonn Keogh, Michail Vlachos, and Gautam Das. 2005. Mining time series data. Data Mining and Knowledge Discovery Handbook 2005. Eds. Oded Maimon, Lior Rokach. Springer. Pages 1069-1103.
• Jessica Lin, Eamonn Keogh, 2004, Finding or not finding rules in time series. Applications of Artificial Intelligence in Finance and Economics (Advances in Econometrics, Volume 19), pp.175-201.
• Jessica Lin, Eamonn Keogh, Stefano Lonardi, Jeffrey P. Lankford, and Donna M. Nystrom. 2004. Visually mining and monitoring massive time series. In Proceedings of the tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '04). ACM, New York, NY, USA, 460-469.
  [Citations]
• Jessica Lin, Eamonn Keogh, Stefano Lonardi, Jeffrey P. Lankford, and Daonna M. Nystrom. 2004. VizTree: a tool for visually mining and monitoring massive time series databases. In Proceedings of the 30th International Conference on Very large Data Bases - Volume 30 (VLDB '04). VLDB Endowment. Pages 1269-1272.
• Jessica Lin, Michail Vlachos, Eamonn Keogh, and Dimitrios Gunopulos. 2004. Iterative incremental clustering of time series. In Proceedings of the IX Conference on Extending Database Technology (EDBT). Lecture Notes in Computer Science, Springer. Pages 106-122.
  [Citations]
• Eamonn Keogh, Jessica Lin, Stefano Lonardi, and Bill Chiu. 2004. We have seen the future, and it is symbolic. In Proceedings of the Second Workshop on Australasian Information Security, Data Mining and Web Intelligence, and Software Internationalisation - Volume 32 (ACSW Frontiers '04), J. Hogan, P. Montague, M. Purvis, and C. Steketee (Eds.), Vol. 32. Australian Computer Society, Inc., Darlinghurst, Australia, Australia, Page 83.
• Eamonn Keogh, Jessica Lin, and Wagner Truppel. 2003. Clustering of time series subsequences is meaningless: implications for past and future research. In Proceedings of the 3rd IEEE International Conference on Data Mining (ICDM). IEEE Computer Society. Pages 115-122.
  [Citations]
• Jessica Lin, Eamonn Keogh, and Wagner Truppel. 2003. (Not) Finding rules in time series: a surprising result with implications for previous and future research. In Proceedings of the 2003 International Conference on Artificial Intelligence. Las Vegas, NV. June 23-26. Pages 55-61.
• Jessica Lin, Vlachos, M, Keogh, E., & Gunopulos, D. 2003. Multi-resolution k-means clustering of time series and application to images. In Proceedings of the 4th SIGKDD Workshop on Multimedia Data Mining, in conjunction with SIGKDD 2003. 10 pages.
• Jessica Lin, Eamonn Keogh, Stefano Lonardi, and Bill Chiu. 2003. A symbolic representation of time series, with implication for streaming algorithms. In Proceedings of the 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery (DMKD '03). ACM, New York, NY, USA. Pages 2-11.
  [Citations]
• Jessica Lin, Eamonn Keogh, and Wagner Truppel. 2003. Clustering of streaming time series is meaningless. In Proceedings of the 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery (DMKD '03). ACM, New York, NY, USA. Pages 2-11.
  [Citations]
• Jessica Lin, Michail Vlachos, Eamonn Keogh, and Dimitrios Gunopulos. 2003. A wavelet-based anytime algorithm for k-means clustering of time series. In Proceedings of the Workshop on Clustering High Dimensional Data and Its Applications, at the 3rd SIAM International Conference on Data Mining. San Francisco, CA. May 3, 2003. 10 pages.
  [Citations]
• Jessica Lin and Gunopulos, D. 2003. Dimensionality reduction by random projection and latent semantic indexing. In Proceedings of the Text Mining Workshop, at the 3rd SIAM International Conference on Data Mining. San Francisco, CA. May 3, 2003. 10 pages.
  [Citations]
• Pranav Patel, Eamonn Keogh, Jessica Lin, and Stefano Lonardi. 2002. Mining motifs in massive time series databases. In Proceedings of the 2002 IEEE International Conference on Data Mining (ICDM '02). IEEE Computer Society, Washington, DC, USA, 370-377.
  [Citations]
• Jessica Lin, Eamonn Keogh, Pranav Patel, and Stefano Lonardi. 2002. Finding motifs in time series. In Proceedings of the 2nd Workshop on Temporal Data Mining, at the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Edmonton, Alberta, Canada. July 23-26, 2002. 11 pages.
  [Citations]