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Eamonn J. Keogh :
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Eamonn J. Keogh , Jessica Lin , Stefano Lonardi , Bill Yuan-chi Chiu We Have Seen the Future, and It Is Symbolic. [Citation Graph (0, 0)][DBLP ] ACSW Frontiers, 2004, pp:83- [Conf ] Ada Wai-Chee Fu , Oscar Tat-Wing Leung , Eamonn J. Keogh , Jessica Lin Finding Time Series Discords Based on Haar Transform. [Citation Graph (0, 0)][DBLP ] ADMA, 2006, pp:31-41 [Conf ] Jessica Lin , Eamonn J. Keogh , Ada Wai-Chee Fu , Helga Van Herle Approximations to Magic: Finding Unusual Medical Time Series. [Citation Graph (0, 0)][DBLP ] CBMS, 2005, pp:329-334 [Conf ] Li Wei , Nitin Kumar , Venkata Nishanth Lolla , Eamonn J. Keogh , Stefano Lonardi , Chotirat (Ann) Ratanamahatana , Helga Van Herle A Practical Tool for Visualizing and Data Mining Medical Time Series. [Citation Graph (0, 0)][DBLP ] CBMS, 2005, pp:341-346 [Conf ] Pablo Viana , Ann Gordon-Ross , Eamonn J. Keogh , Edna Barros , Frank Vahid Configurable cache subsetting for fast cache tuning. [Citation Graph (0, 0)][DBLP ] DAC, 2006, pp:695-700 [Conf ] Jessica Lin , Eamonn J. Keogh , Stefano Lonardi , Bill Yuan-chi Chiu A symbolic representation of time series, with implications for streaming algorithms. [Citation Graph (0, 0)][DBLP ] DMKD, 2003, pp:2-11 [Conf ] Jessica Lin , Eamonn J. Keogh , Wagner Truppel Clustering of streaming time series is meaningless. [Citation Graph (0, 0)][DBLP ] DMKD, 2003, pp:56-65 [Conf ] Dragomir Yankov , Dennis DeCoste , Eamonn J. Keogh Ensembles of Nearest Neighbor Forecasts. [Citation Graph (0, 0)][DBLP ] ECML, 2006, pp:545-556 [Conf ] Jessica Lin , Michail Vlachos , Eamonn J. Keogh , Dimitrios Gunopulos Iterative Incremental Clustering of Time Series. [Citation Graph (0, 0)][DBLP ] EDBT, 2004, pp:106-122 [Conf ] Eamonn J. Keogh , Harry Hochheiser , Ben Shneiderman An Augmented Visual Query Mechanism for Finding Patterns in Time Series Data. [Citation Graph (0, 0)][DBLP ] FQAS, 2002, pp:240-250 [Conf ] Chotirat (Ann) Ratanamahatana , Eamonn J. Keogh Multimedia Retrieval Using Time Series Representation and Relevance Feedback. [Citation Graph (0, 0)][DBLP ] ICADL, 2005, pp:400-405 [Conf ] Jessica Lin , Eamonn J. Keogh , Wagner Truppel (Not) Finding Rules in Time Series: A Surprising Result with Implications for Previous and Future Research. [Citation Graph (0, 0)][DBLP ] IC-AI, 2003, pp:55-61 [Conf ] Themistoklis Palpanas , Michail Vlachos , Eamonn J. Keogh , Dimitrios Gunopulos , Wagner Truppel Online Amnesic Approximation of Streaming Time Series. [Citation Graph (0, 0)][DBLP ] ICDE, 2004, pp:338-349 [Conf ] Eamonn J. Keogh , Selina Chu , David Hart , Michael J. Pazzani An Online Algorithm for Segmenting Time Series. [Citation Graph (0, 0)][DBLP ] ICDM, 2001, pp:289-296 [Conf ] Eamonn J. Keogh , Jessica Lin , Ada Wai-Chee Fu HOT SAX: Efficiently Finding the Most Unusual Time Series Subsequence. [Citation Graph (0, 0)][DBLP ] ICDM, 2005, pp:226-233 [Conf ] Eamonn J. Keogh , Jessica Lin , Wagner Truppel Clustering of Time Series Subsequences is Meaningless: Implications for Previous and Future Research. [Citation Graph (0, 0)][DBLP ] ICDM, 2003, pp:115-122 [Conf ] Longin Jan Latecki , Vasileios Megalooikonomou , Qiang Wang , Rolf Lakämper , Chotirat (Ann) Ratanamahatana , Eamonn J. Keogh Partial Elastic Matching of Time Series. [Citation Graph (0, 0)][DBLP ] ICDM, 2005, pp:701-704 [Conf ] Pranav Patel , Eamonn J. Keogh , Jessica Lin , Stefano Lonardi Mining Motifs in Massive Time Series Databases. [Citation Graph (0, 0)][DBLP ] ICDM, 2002, pp:370-377 [Conf ] Li Wei , Eamonn J. Keogh , Helga Van Herle , Agenor Mafra-Neto Atomic Wedgie: Efficient Query Filtering for Streaming Times Series. [Citation Graph (0, 0)][DBLP ] ICDM, 2005, pp:490-497 [Conf ] Li Wei , Eamonn J. Keogh , Xiaopeng Xi SAXually Explicit Images: Finding Unusual Shapes. [Citation Graph (0, 0)][DBLP ] ICDM, 2006, pp:711-720 [Conf ] Dragomir Yankov , Eamonn J. Keogh Manifold Clustering of Shapes. [Citation Graph (0, 0)][DBLP ] ICDM, 2006, pp:1167-1171 [Conf ] Eamonn J. Keogh , Li Wei , Xiaopeng Xi , Stefano Lonardi , Jin Shieh , Scott Sirowy Intelligent Icons: Integrating Lite-Weight Data Mining and Visualization into GUI Operating Systems. [Citation Graph (0, 0)][DBLP ] ICDM, 2006, pp:912-916 [Conf ] Ken Ueno , Xiaopeng Xi , Eamonn J. Keogh , Dah-Jye Lee Anytime Classification Using the Nearest Neighbor Algorithm with Applications to Stream Mining. [Citation Graph (0, 0)][DBLP ] ICDM, 2006, pp:623-632 [Conf ] Li Wei , John Handley , Nathaniel Martin , Tong Sun , Eamonn J. Keogh Clustering Workflow Requirements Using Compression Dissimilarity Measure. [Citation Graph (0, 0)][DBLP ] ICDM Workshops, 2006, pp:50-54 [Conf ] Xiaopeng Xi , Eamonn J. Keogh , Christian R. Shelton , Li Wei , Chotirat Ann Ratanamahatana Fast time series classification using numerosity reduction. [Citation Graph (0, 0)][DBLP ] ICML, 2006, pp:1033-1040 [Conf ] Eamonn J. Keogh Fast Similarity Search in the Presence of Longitudinal Scaling in Time Series Databases. [Citation Graph (0, 0)][DBLP ] ICTAI, 1997, pp:578-584 [Conf ] Dragomir Yankov , Eamonn J. Keogh , Stefano Lonardi , Ada Wai-Chee Fu Dot Plots for Time Series Analysis. [Citation Graph (0, 0)][DBLP ] ICTAI, 2005, pp:159-168 [Conf ] Jiyuan An , Hanxiong Chen , Kazutaka Furuse , Nobuo Ohbo , Eamonn J. Keogh Grid-Based Indexing for Large Time Series Databases. [Citation Graph (0, 0)][DBLP ] IDEAL, 2003, pp:614-621 [Conf ] Aris Anagnostopoulos , Michail Vlachos , Marios Hadjieleftheriou , Eamonn J. Keogh , Philip S. Yu Global distance-based segmentation of trajectories. [Citation Graph (0, 0)][DBLP ] KDD, 2006, pp:34-43 [Conf ] Bill Yuan-chi Chiu , Eamonn J. Keogh , Stefano Lonardi Probabilistic discovery of time series motifs. [Citation Graph (0, 0)][DBLP ] KDD, 2003, pp:493-498 [Conf ] Eamonn J. Keogh , Selina Chu , Michael J. Pazzani Ensemble-index: a new approach to indexing large databases. [Citation Graph (0, 0)][DBLP ] KDD, 2001, pp:117-125 [Conf ] Eamonn J. Keogh , Shruti Kasetty On the need for time series data mining benchmarks: a survey and empirical demonstration. [Citation Graph (0, 0)][DBLP ] KDD, 2002, pp:102-111 [Conf ] Eamonn J. Keogh , Stefano Lonardi , Bill Yuan-chi Chiu Finding surprising patterns in a time series database in linear time and space. [Citation Graph (0, 0)][DBLP ] KDD, 2002, pp:550-556 [Conf ] Eamonn J. Keogh , Stefano Lonardi , Chotirat (Ann) Ratanamahatana Towards parameter-free data mining. [Citation Graph (0, 0)][DBLP ] KDD, 2004, pp:206-215 [Conf ] Eamonn J. Keogh , Michael J. Pazzani Scaling up dynamic time warping for datamining applications. [Citation Graph (0, 0)][DBLP ] KDD, 2000, pp:285-289 [Conf ] Eamonn J. Keogh , Michael J. Pazzani An Enhanced Representation of Time Series Which Allows Fast and Accurate Classification, Clustering and Relevance Feedback. [Citation Graph (0, 0)][DBLP ] KDD, 1998, pp:239-243 [Conf ] Eamonn J. Keogh , Padhraic Smyth A Probabilistic Approach to Fast Pattern Matching in Time Series Databases. [Citation Graph (0, 0)][DBLP ] KDD, 1997, pp:24-30 [Conf ] Jessica Lin , Eamonn J. Keogh , Stefano Lonardi , Jeffrey P. Lankford , Donna M. Nystrom Visually mining and monitoring massive time series. [Citation Graph (0, 0)][DBLP ] KDD, 2004, pp:460-469 [Conf ] Michail Vlachos , Marios Hadjieleftheriou , Dimitrios Gunopulos , Eamonn J. Keogh Indexing multi-dimensional time-series with support for multiple distance measures. [Citation Graph (0, 0)][DBLP ] KDD, 2003, pp:216-225 [Conf ] Li Wei , Eamonn J. Keogh Semi-supervised time series classification. [Citation Graph (0, 0)][DBLP ] KDD, 2006, pp:748-753 [Conf ] Chotirat (Ann) Ratanamahatana , Eamonn J. Keogh Using Relevance Feedback to Learn Both the Distance Measure and the Query in Multimedia Databases. [Citation Graph (0, 0)][DBLP ] KES (2), 2005, pp:16-23 [Conf ] Petko Bakalov , Marios Hadjieleftheriou , Eamonn J. Keogh , Vassilis J. Tsotras Efficient trajectory joins using symbolic representations. [Citation Graph (0, 0)][DBLP ] Mobile Data Management, 2005, pp:86-93 [Conf ] Eamonn J. Keogh , Michael J. Pazzani A Simple Dimensionality Reduction Technique for Fast Similarity Search in Large Time Series Databases. [Citation Graph (0, 0)][DBLP ] PAKDD, 2000, pp:122-133 [Conf ] 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. [Citation Graph (0, 0)][DBLP ] PAKDD, 2005, pp:333-342 [Conf ] 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. [Citation Graph (0, 0)][DBLP ] PAKDD, 2005, pp:771-777 [Conf ] Eamonn J. Keogh Efficiently Finding Arbitrarily Scaled Patterns in Massive Time Series Databases. [Citation Graph (0, 0)][DBLP ] PKDD, 2003, pp:253-265 [Conf ] Eamonn J. Keogh Recent Advances in Mining Time Series Data. [Citation Graph (0, 0)][DBLP ] PKDD, 2005, pp:6- [Conf ] Eamonn J. Keogh , Michael J. Pazzani Scaling up Dynamic Time Warping to Massive Dataset. [Citation Graph (0, 0)][DBLP ] PKDD, 1999, pp:1-11 [Conf ] Longin Jan Latecki , Vasilis Megalooikonomou , Qiang Wang , Rolf Lakämper , Chotirat (Ann) Ratanamahatana , Eamonn J. Keogh Elastic Partial Matching of Time Series. [Citation Graph (0, 0)][DBLP ] PKDD, 2005, pp:577-584 [Conf ] Jessica Lin , Eamonn J. Keogh Group SAX: Extending the Notion of Contrast Sets to Time Series and Multimedia Data. [Citation Graph (0, 0)][DBLP ] PKDD, 2006, pp:284-296 [Conf ] Eamonn J. Keogh Indexing and Mining Time Series. [Citation Graph (0, 0)][DBLP ] SBBD, 2002, pp:9- [Conf ] Eamonn J. Keogh A Gentle Introduction to Machine Learning and Data Mining for the Database Community. [Citation Graph (0, 0)][DBLP ] SBBD, 2003, pp:2- [Conf ] Selina Chu , Eamonn J. Keogh , David Hart , Michael J. Pazzani Iterative Deepening Dynamic Time Warping for Time Series. [Citation Graph (0, 0)][DBLP ] SDM, 2002, pp:- [Conf ] Chotirat (Ann) Ratanamahatana , Eamonn J. Keogh Three Myths about Dynamic Time Warping Data Mining. [Citation Graph (0, 0)][DBLP ] SDM, 2005, pp:- [Conf ] 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. [Citation Graph (0, 0)][DBLP ] SDM, 2005, pp:- [Conf ] Chotirat (Ann) Ratanamahatana , Eamonn J. Keogh Making Time-Series Classification More Accurate Using Learned Constraints. [Citation Graph (0, 0)][DBLP ] SDM, 2004, pp:- [Conf ] Eamonn J. Keogh , Michael J. Pazzani Relevance Feedback Retrieval of Time Series Data. [Citation Graph (0, 0)][DBLP ] SIGIR, 1999, pp:183-190 [Conf ] Eamonn J. Keogh , Kaushik Chakrabarti , Sharad Mehrotra , Michael J. Pazzani Locally Adaptive Dimensionality Reduction for Indexing Large Time Series Databases. [Citation Graph (0, 0)][DBLP ] SIGMOD Conference, 2001, pp:151-162 [Conf ] Eamonn J. Keogh , Michael J. Pazzani An Indexing Scheme for Fast Similarity Search in Large Time Series Databases. [Citation Graph (0, 0)][DBLP ] SSDBM, 1999, pp:56-67 [Conf ] Li Wei , Nitin Kumar , Venkata Nishanth Lolla , Eamonn J. Keogh , Stefano Lonardi , Chotirat (Ann) Ratanamahatana Assumption-Free Anomaly Detection in Time Series. [Citation Graph (0, 0)][DBLP ] SSDBM, 2005, pp:237-240 [Conf ] Eamonn J. Keogh Visualization and Mining of Temporal Data. [Citation Graph (0, 0)][DBLP ] IEEE Visualization, 2005, pp:126- [Conf ] Ada Wai-Chee Fu , Eamonn J. Keogh , Leo Yung Hang Lau , Chotirat (Ann) Ratanamahatana Scaling and Time Warping in Time Series Querying. [Citation Graph (0, 0)][DBLP ] VLDB, 2005, pp:649-660 [Conf ] Eamonn J. Keogh Exact Indexing of Dynamic Time Warping. [Citation Graph (0, 0)][DBLP ] VLDB, 2002, pp:406-417 [Conf ] Eamonn J. Keogh A Decade of Progress in Indexing and Mining Large Time Series Databases. [Citation Graph (0, 0)][DBLP ] VLDB, 2006, pp:1268- [Conf ] Eamonn J. Keogh , Li Wei , Xiaopeng Xi , Sang-Hee Lee , Michail Vlachos LB_Keogh Supports Exact Indexing of Shapes under Rotation Invariance with Arbitrary Representations and Distance Measures. [Citation Graph (0, 0)][DBLP ] VLDB, 2006, pp:882-893 [Conf ] 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. [Citation Graph (0, 0)][DBLP ] VLDB, 2004, pp:1269-1272 [Conf ] Eamonn J. Keogh , Themis Palpanas , Victor B. Zordan , Dimitrios Gunopulos , Marc Cardle Indexing Large Human-Motion Databases. [Citation Graph (0, 0)][DBLP ] VLDB, 2004, pp:780-791 [Conf ] Jiyuan An , Yi-Ping Phoebe Chen , Eamonn J. Keogh A Grid-Based Index Method for Time Warping Distance. [Citation Graph (0, 0)][DBLP ] WAIM, 2004, pp:65-75 [Conf ] Eamonn J. Keogh , Shruti Kasetty On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration. [Citation Graph (0, 0)][DBLP ] Data Min. Knowl. Discov., 2003, v:7, n:4, pp:349-371 [Journal ] Anthony J. Bagnall , Chotirat (Ann) Ratanamahatana , Eamonn J. Keogh , Stefano Lonardi , Gareth J. Janacek A Bit Level Representation for Time Series Data Mining with Shape Based Similarity. [Citation Graph (0, 0)][DBLP ] Data Min. Knowl. Discov., 2006, v:13, n:1, pp:11-40 [Journal ] Eamonn J. Keogh , Michael J. Pazzani Learning the Structure of Augmented Bayesian Classifiers. [Citation Graph (0, 0)][DBLP ] International Journal on Artificial Intelligence Tools, 2002, v:11, n:4, pp:587-601 [Journal ] Jessica Lin , Eamonn J. Keogh , Stefano Lonardi Visualizing and discovering non-trivial patterns in large time series databases. [Citation Graph (0, 0)][DBLP ] Information Visualization, 2005, v:4, n:2, pp:61-82 [Journal ] Li Wei , Eamonn J. Keogh , Xiaopeng Xi , Stefano Lonardi Integrating Lite-Weight but Ubiquitous Data Mining into GUI Operating Systems. [Citation Graph (0, 0)][DBLP ] J. UCS, 2005, v:11, n:11, pp:1820-1834 [Journal ] Eamonn J. Keogh , Kaushik Chakrabarti , Michael J. Pazzani , Sharad Mehrotra Dimensionality Reduction for Fast Similarity Search in Large Time Series Databases. [Citation Graph (0, 0)][DBLP ] Knowl. Inf. Syst., 2001, v:3, n:3, pp:263-286 [Journal ] Eamonn J. Keogh , Jessica Lin Clustering of time-series subsequences is meaningless: implications for previous and future research. [Citation Graph (0, 0)][DBLP ] Knowl. Inf. Syst., 2005, v:8, n:2, pp:154-177 [Journal ] Eamonn J. Keogh , Chotirat (Ann) Ratanamahatana Exact indexing of dynamic time warping. [Citation Graph (0, 0)][DBLP ] Knowl. Inf. Syst., 2005, v:7, n:3, pp:358-386 [Journal ] Eamonn J. Keogh , Jessica Lin , Sang-Hee Lee , Helga Van Herle Finding the most unusual time series subsequence: algorithms and applications. [Citation Graph (0, 0)][DBLP ] Knowl. Inf. Syst., 2007, v:11, n:1, pp:1-27 [Journal ] Eamonn J. Keogh Guest Editorial. [Citation Graph (0, 0)][DBLP ] Machine Learning, 2005, v:58, n:2-3, pp:103-105 [Journal ] Kaushik Chakrabarti , Eamonn J. Keogh , Sharad Mehrotra , Michael J. Pazzani Locally adaptive dimensionality reduction for indexing large time series databases. [Citation Graph (0, 0)][DBLP ] ACM Trans. Database Syst., 2002, v:27, n:2, pp:188-228 [Journal ] Michail Vlachos , Marios Hadjieleftheriou , Dimitrios Gunopulos , Eamonn J. Keogh Indexing Multidimensional Time-Series. [Citation Graph (0, 0)][DBLP ] VLDB J., 2006, v:15, n:1, pp:1-20 [Journal ] Dragomir Yankov , Eamonn J. Keogh , Jose Medina , Bill Chiu , Victor B. Zordan Detecting time series motifs under uniform scaling. [Citation Graph (0, 0)][DBLP ] KDD, 2007, pp:844-853 [Conf ] Eamonn J. Keogh Data mining and information retrieval in time series/multimedia databases. [Citation Graph (0, 0)][DBLP ] ACM Multimedia, 2006, pp:10- [Conf ] Michail Vlachos , Bahar Taneri , Eamonn J. Keogh , Philip S. Yu Visual Exploration of Genomic Data. [Citation Graph (0, 0)][DBLP ] PKDD, 2007, pp:613-620 [Conf ] Yingyi Bu , Oscar Tat-Wing Leung , Ada Wai-Chee Fu , Eamonn J. Keogh , Jian Pei , Sam Meshkin WAT: Finding Top-K Discords in Time Series Database. [Citation Graph (0, 0)][DBLP ] SDM, 2007, pp:- [Conf ] Xiaopeng Xi , Eamonn J. Keogh , Li Wei , Agenor Mafra-Neto Finding Motifs in a Database of Shapes. [Citation Graph (0, 0)][DBLP ] SDM, 2007, pp:- [Conf ] Dragomir Yankov , Eamonn J. Keogh , Li Wei , Xiaopeng Xi , Wendy L. Hodges Fast Best-Match Shape Searching in Rotation Invariant Metric Spaces. [Citation Graph (0, 0)][DBLP ] SDM, 2007, pp:- [Conf ] Eamonn J. Keogh , Stefano Lonardi , Chotirat Ann Ratanamahatana , Li Wei , Sang-Hee Lee , John Handley Compression-based data mining of sequential data. [Citation Graph (0, 0)][DBLP ] Data Min. Knowl. Discov., 2007, v:14, n:1, pp:99-129 [Journal ] Jessica Lin , Eamonn J. Keogh , Li Wei , Stefano Lonardi Experiencing SAX: a novel symbolic representation of time series. [Citation Graph (0, 0)][DBLP ] Data Min. Knowl. Discov., 2007, v:15, n:2, pp:107-144 [Journal ] Li Wei , Eamonn J. Keogh , Helga Van Herle , Agenor Mafra-Neto , Russell J. Abbott Efficient query filtering for streaming time series with applications to semisupervised learning of time series classifiers. [Citation Graph (0, 0)][DBLP ] Knowl. Inf. Syst., 2007, v:11, n:3, pp:313-344 [Journal ] Ada Wai-Chee Fu , Eamonn J. Keogh , Leo Yung Hang Lau , Chotirat (Ann) Ratanamahatana , Raymond Chi-Wing Wong Scaling and time warping in time series querying. [Citation Graph (0, 0)][DBLP ] VLDB J., 2008, v:0, n:, pp:- [Journal ] TS2-tree - an efficient similarity based organization for trajectory data. [Citation Graph (, )][DBLP ] Disk Aware Discord Discovery: Finding Unusual Time Series in Terabyte Sized Datasets. [Citation Graph (, )][DBLP ] Locally Constrained Support Vector Clustering. [Citation Graph (, )][DBLP ] Finding Time Series Motifs in Disk-Resident Data. [Citation Graph (, )][DBLP ] Real-Time Classification of Streaming Sensor Data. [Citation Graph (, )][DBLP ] Using CAPTCHAs to Index Cultural Artifacts. [Citation Graph (, )][DBLP ] Augmenting Historical Manuscripts with Automatic Hyperlinks. [Citation Graph (, )][DBLP ] Annotating historical archives of images. [Citation Graph (, )][DBLP ] Finding centuries-old hyperlinks with a novel semi-supervised learning technique. [Citation Graph (, )][DBLP ] Time series shapelets: a new primitive for data mining. [Citation Graph (, )][DBLP ] i SAX: indexing and mining terabyte sized time series. [Citation Graph (, )][DBLP ] Augmenting the generalized hough transform to enable the mining of petroglyphs. [Citation Graph (, )][DBLP ] Online discovery and maintenance of time series motifs. [Citation Graph (, )][DBLP ] The Asymmetric Approximate Anytime Join: A New Primitive with Applications to Data Mining. [Citation Graph (, )][DBLP ] Autocannibalistic and Anyspace Indexing Algorithms with Application to Sensor Data Mining. [Citation Graph (, )][DBLP ] Exact Discovery of Time Series Motifs. [Citation Graph (, )][DBLP ] A Compression Based Distance Measure for Texture. [Citation Graph (, )][DBLP ] Efficiently finding unusual shapes in large image databases. [Citation Graph (, )][DBLP ] i SAX: disk-aware mining and indexing of massive time series datasets. [Citation Graph (, )][DBLP ] Search in 0.006secs, Finished in 0.608secs