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Note: Links lead to the DBLP on the Web. Eamonn J. Keogh Jessica Lin , Eamonn J. Keogh, Ada Wai-Chee Fu , Helga Van Herle : Approximations to Magic: Finding Unusual Medical Time Series. CBMS 2005 : 329-334 Li Wei , Nitin Kumar , Venkata Lolla , Eamonn J. Keogh, Stefano Lonardi , Chotirat (Ann) Ratanamahatana , Helga Van Herle : A Practical Tool for Visualizing and Data Mining Medical Time Series. CBMS 2005 : 341-346 Chotirat (Ann) Ratanamahatana , Eamonn J. Keogh: Multimedia Retrieval Using Time Series Representation and Relevance Feedback. ICADL 2005 : 400-405 Eamonn J. Keogh, Jessica Lin , Ada Wai-Chee Fu : HOT SAX: Efficiently Finding the Most Unusual Time Series Subsequence. ICDM 2005 : 226-233 Li Wei , Eamonn J. Keogh, Helga Van Herle , Agenor Mafra-Neto : Atomic Wedgie: Efficient Query Filtering for Streaming Times Series. ICDM 2005 : 490-497 Longin Jan Latecki , Vasileios Megalooikonomou , Qiang Wang , Rolf Lakämper , Chotirat (Ann) Ratanamahatana , Eamonn J. Keogh: Partial Elastic Matching of Time Series. ICDM 2005 : 701-704 Dragomir Yankov , Eamonn J. Keogh, Stefano Lonardi , Ada Waichee Fu : Dot Plots for Time Series Analysis. ICTAI 2005 : 159-168 Eamonn J. Keogh: Visualization and Mining of Temporal Data. IEEE Visualization 2005 : 126 Chotirat (Ann) Ratanamahatana , Eamonn J. Keogh: Using Relevance Feedback to Learn Both the Distance Measure and the Query in Multimedia Databases. KES (2) 2005 : 16-23 Petko Bakalov , Marios Hadjieleftheriou , Eamonn J. Keogh, Vassilis J. Tsotras : Efficient trajectory joins using symbolic representations. Mobile Data Management 2005 : 86-93 Jessica Lin , Michail Vlachos , Eamonn J. Keogh, Dimitrios Gunopulos , Jian-Wei Liu , Shou-Jian Yu , Jia-Jin Le : A MPAA-Based Iterative Clustering Algorithm Augmented by Nearest Neighbors Search for Time-Series Data Streams. PAKDD 2005 : 333-342 Chotirat (Ann) Ratanamahatana , Eamonn J. Keogh, Anthony J. Bagnall , Stefano Lonardi : A Novel Bit Level Time Series Representation with Implication of Similarity Search and Clustering. PAKDD 2005 : 771-777 Longin Jan Latecki , Vasilis Megalooikonomou , Qiang Wang , Rolf Lakämper , Chotirat (Ann) Ratanamahatana , Eamonn J. Keogh: Elastic Partial Matching of Time Series. PKDD 2005 : 577-584 Eamonn J. Keogh: Recent Advances in Mining Time Series Data. PKDD 2005 : 6 Chotirat (Ann) Ratanamahatana , Eamonn J. Keogh: Three Myths about Dynamic Time Warping Data Mining. SDM 2005 Nitin Kumar , Venkata Nishanth Lolla , Eamonn J. Keogh, Stefano Lonardi , Chotirat (Ann) Ratanamahatana : Time-series Bitmaps: a Practical Visualization Tool for Working with Large Time Series Databases. SDM 2005 Li Wei , Nitin Kumar , Venkata Lolla , Eamonn J. Keogh, Stefano Lonardi , Chotirat (Ann) Ratanamahatana : Assumption-Free Anomaly Detection in Time Series. SSDBM 2005 : 237-240 Ada Wai-Chee Fu , Eamonn J. Keogh, Leo Yung Hang Lau , Chotirat (Ann) Ratanamahatana : Scaling and Time Warping in Time Series Querying. VLDB 2005 : 649-660 Michail Vlachos , Marios Hadjieleftheriou , Eamonn J. Keogh, Dimitrios Gunopulos : Indexing Multi-Dimensional Trajectories for Similarity Queries. Spatial Databases 2005 : 107-128 Chotirat (Ann) Ratanamahatana , Jessica Lin , Dimitrios Gunopulos , Eamonn J. Keogh, Michail Vlachos , Gautam Das : Mining Time Series Data. The Data Mining and Knowledge Discovery Handbook 2005 : 1069-1103 Jessica Lin , Eamonn J. Keogh, Stefano Lonardi : Visualizing and discovering non-trivial patterns in large time series databases. Information Visualization 4 (2): 61-82 (2005) Li Wei , Eamonn J. Keogh, Xiaopeng Xi , Stefano Lonardi : Integrating Lite-Weight but Ubiquitous Data Mining into GUI Operating Systems. J. UCS 11 (11): 1820-1834 (2005) Eamonn J. Keogh, Chotirat (Ann) Ratanamahatana : Exact indexing of dynamic time warping. Knowl. Inf. Syst. 7 (3): 358-386 (2005) Eamonn J. Keogh, Jessica Lin : Clustering of time-series subsequences is meaningless: implications for previous and future research. Knowl. Inf. Syst. 8 (2): 154-177 (2005) Eamonn J. Keogh: Guest Editorial. Machine Learning 58 (2-3): 103-105 (2005) Eamonn J. Keogh, Jessica Lin , Stefano Lonardi , Bill Yuan-chi Chiu : We Have Seen the Future, and It Is Symbolic. ACSW Frontiers 2004 : 83 Jessica Lin , Michail Vlachos , Eamonn J. Keogh, Dimitrios Gunopulos : Iterative Incremental Clustering of Time Series. EDBT 2004 : 106-122 Themistoklis Palpanas , Michail Vlachos , Eamonn J. Keogh, Dimitrios Gunopulos , Wagner Truppel : Online Amnesic Approximation of Streaming Time Series. ICDE 2004 : 338-349 Eamonn J. Keogh, Stefano Lonardi , Chotirat (Ann) Ratanamahatana : Towards parameter-free data mining. KDD 2004 : 206-215 Jessica Lin , Eamonn J. Keogh, Stefano Lonardi , Jeffrey P. Lankford , Donna M. Nystrom : Visually mining and monitoring massive time series. KDD 2004 : 460-469 Chotirat (Ann) Ratanamahatana , Eamonn J. Keogh: Making Time-Series Classification More Accurate Using Learned Constraints. SDM 2004 Jessica Lin , Eamonn J. Keogh, Stefano Lonardi , Jeffrey P. Lankford , Donna M. Nystrom : VizTree: a Tool for Visually Mining and Monitoring Massive Time Series Databases. VLDB 2004 : 1269-1272 Eamonn J. Keogh, Themis Palpanas , Victor B. Zordan , Dimitrios Gunopulos , Marc Cardle : Indexing Large Human-Motion Databases. VLDB 2004 : 780-791 Jiyuan An , Yi-Ping Phoebe Chen , Eamonn J. Keogh: A Grid-Based Index Method for Time Warping Distance. WAIM 2004 : 65-75 Jessica Lin , Eamonn J. Keogh, Stefano Lonardi , Bill Yuan-chi Chiu : A symbolic representation of time series, with implications for streaming algorithms. DMKD 2003 : 2-11 Jessica Lin , Eamonn J. Keogh, Wagner Truppel : Clustering of streaming time series is meaningless. DMKD 2003 : 56-65 Jessica Lin , Eamonn J. Keogh, Wagner Truppel : (Not) Finding Rules in Time Series: A Surprising Result with Implications for Previous and Future Research. IC-AI 2003 : 55-61 Eamonn J. Keogh, Jessica Lin , Wagner Truppel : Clustering of Time Series Subsequences is Meaningless: Implications for Previous and Future Research. ICDM 2003 : 115-122 Jiyuan An , Hanxiong Chen , Kazutaka Furuse , Nobuo Ohbo , Eamonn J. Keogh: Grid-Based Indexing for Large Time Series Databases. IDEAL 2003 : 614-621 Michail Vlachos , Marios Hadjieleftheriou , Dimitrios Gunopulos , Eamonn J. Keogh: Indexing multi-dimensional time-series with support for multiple distance measures. KDD 2003 : 216-225 Bill Yuan-chi Chiu , Eamonn J. Keogh, Stefano Lonardi : Probabilistic discovery of time series motifs. KDD 2003 : 493-498 Eamonn J. Keogh: Efficiently Finding Arbitrarily Scaled Patterns in Massive Time Series Databases. PKDD 2003 : 253-265 Eamonn J. Keogh: A Gentle Introduction to Machine Learning and Data Mining for the Database Community. SBBD 2003 : 2 Eamonn J. Keogh, Shruti Kasetty : On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration. Data Min. Knowl. Discov. 7 (4): 349-371 (2003) Eamonn J. Keogh, Harry Hochheiser , Ben Shneiderman : An Augmented Visual Query Mechanism for Finding Patterns in Time Series Data. FQAS 2002 : 240-250 Pranav Patel , Eamonn J. Keogh, Jessica Lin , Stefano Lonardi : Mining Motifs in Massive Time Series Databases. ICDM 2002 : 370-377 Eamonn J. Keogh, Shruti Kasetty : On the need for time series data mining benchmarks: a survey and empirical demonstration. KDD 2002 : 102-111 Eamonn J. Keogh, Stefano Lonardi , Bill Yuan-chi Chiu : Finding surprising patterns in a time series database in linear time and space. KDD 2002 : 550-556 Eamonn J. Keogh: Indexing and Mining Time Series. SBBD 2002 : 9 Selina Chu , Eamonn J. Keogh, David Hart , Michael J. Pazzani : Iterative Deepening Dynamic Time Warping for Time Series. SDM 2002 Eamonn J. Keogh: Exact Indexing of Dynamic Time Warping. VLDB 2002 : 406-417 Kaushik Chakrabarti , Eamonn J. Keogh, Sharad Mehrotra , Michael J. Pazzani : Locally adaptive dimensionality reduction for indexing large time series databases. ACM Trans. Database Syst. 27 (2): 188-228 (2002) Eamonn J. Keogh, Michael J. Pazzani : Learning the Structure of Augmented Bayesian Classifiers. International Journal on Artificial Intelligence Tools 11 (4): 587-601 (2002) Eamonn J. Keogh, Selina Chu , David Hart , Michael J. Pazzani : An Online Algorithm for Segmenting Time Series. ICDM 2001 : 289-296 Eamonn J. Keogh, Selina Chu , Michael J. Pazzani : Ensemble-index: a new approach to indexing large databases. KDD 2001 : 117-125 Eamonn J. Keogh, Kaushik Chakrabarti , Sharad Mehrotra , Michael J. Pazzani : Locally Adaptive Dimensionality Reduction for Indexing Large Time Series Databases. SIGMOD Conference 2001 : 151-162 Eamonn J. Keogh, Kaushik Chakrabarti , Michael J. Pazzani , Sharad Mehrotra : Dimensionality Reduction for Fast Similarity Search in Large Time Series Databases. Knowl. Inf. Syst. 3 (3): 263-286 (2001) Eamonn J. Keogh, Michael J. Pazzani : Scaling up dynamic time warping for datamining applications. KDD 2000 : 285-289 Eamonn J. Keogh, Michael J. Pazzani : A Simple Dimensionality Reduction Technique for Fast Similarity Search in Large Time Series Databases. PAKDD 2000 : 122-133 Eamonn J. Keogh, Michael J. Pazzani : Scaling up Dynamic Time Warping to Massive Dataset. PKDD 1999 : 1-11 Eamonn J. Keogh, Michael J. Pazzani : Relevance Feedback Retrieval of Time Series Data. SIGIR 1999 : 183-190 Eamonn J. Keogh, Michael J. Pazzani : An Indexing Scheme for Fast Similarity Search in Large Time Series Databases. SSDBM 1999 : 56-67 Eamonn J. Keogh, Michael J. Pazzani : An Enhanced Representation of Time Series Which Allows Fast and Accurate Classification, Clustering and Relevance Feedback. KDD 1998 : 239-243 Eamonn J. Keogh: Fast Similarity Search in the Presence of Longitudinal Scaling in Time Series Databases. ICTAI 1997 : 578-584 Eamonn J. Keogh, Padhraic Smyth : A Probabilistic Approach to Fast Pattern Matching in Time Series Databases. 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