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Eamonn J. Keogh

Publication record assembled from the DBLP archive of ranked conferences.

Papers indexed

169

Venues

33

Active years

1997–2025

Best venue rank

A*

Where they publish

Papers

169 indexed papers, newest first.

YearVenueTitleAuthors
2025KDDFinding Repeated Structures in Time Series: Algorithms and Applications: A Unifying View of Time Series Motifs/Shapelets/Chains and Similar Primitives.Eamonn J. Keogh
2024CIKMA Systematic Evaluation of Generated Time Series and Their Effects in Self-Supervised Pretraining.Audrey Der, Chin-Chia Michael Yeh, Xin Dai, Huiyuan Chen, Yan Zheng, Yujie Fan, Zhongfang Zhuang, Vivian Lai, Junpeng Wang, Liang Wang, Wei Zhang, Eamonn J. Keogh
2024DSAATime Series Data Mining: A Unifying View.Eamonn J. Keogh
2024ICDMMatrix Profile for Anomaly Detection on Multidimensional Time Series.Chin-Chia Michael Yeh, Audrey Der, Uday Singh Saini, Vivian Lai, Yan Zheng, Junpeng Wang, Xin Dai, Zhongfang Zhuang, Yujie Fan, Huiyuan Chen, Prince Osei Aboagye, Liang Wang, Wei Zhang, Eamonn J. Keogh
2024SDMPUPAE: Intuitive and Actionable Explanations for Time Series Anomalies.Audrey Der, Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Zhongfang Zhuang, Liang Wang, Wei Zhang, Eamonn J. Keogh
2023FCCMFeature Extraction Accelerator for Streaming Time Series.Prithviraj Yuvaraj, Amin Akalantar, Eamonn J. Keogh, Philip Brisk
2023ICDMMatrix Profile XXX: MADRID: A Hyper-Anytime and Parameter-Free Algorithm to Find Time Series Anomalies of all Lengths.Yue Lu, Thirumalai Vinjamoor Akhil Srinivas, Takaaki Nakamura, Makoto Imamura, Eamonn J. Keogh
2023ICDMMatrix Profile XXIX: CSadaf Tafazoli, Yue Lu, Renjie Wu, Thirumalai Vinjamoor Akhil Srinivas, Hannah Dela Cruz, Ryan Mercer, Eamonn J. Keogh
2023KDDGetting an h-Index of 100 in 20 Years or Less!Eamonn J. Keogh
2023SDMMatrix Profile XXVIII: Discovering Multi-Dimensional Time Series Anomalies withSadaf Tafazoli, Eamonn J. Keogh
2022ICDEWhen is Early Classification of Time Series Meaningful? (Extended Abstract).Renjie Wu, Audrey Der, Eamonn J. Keogh
2022ICDECurrent Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of Progress (Extended Abstract).Renjie Wu, Eamonn J. Keogh
2022ICDMMatrix Profile XXV: Introducing Novelets: A Primitive that Allows Online Detection of Emerging Behaviors in Time Series.Ryan Mercer, Eamonn J. Keogh
2022ICDMMatrix Profile XXVI: Mplots: Scaling Time Series Similarity Matrices to Massive Data.Maryam Shahcheraghi, Ryan Mercer, Joo Manuel De Almeida Rodrigues, Audrey Der, Hugo Filipe Silveira Gamboa, Zachary Zimmerman, Eamonn J. Keogh
2022KDDMatrix Profile XXIV: Scaling Time Series Anomaly Detection to Trillions of Datapoints and Ultra-fast Arriving Data Streams.Yue Lu, Renjie Wu, Abdullah Mueen, Maria A. Zuluaga, Eamonn J. Keogh
2022SDMError-bounded Approximate Time Series Joins using Compact Dictionary Representations of Time Series.Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, Eamonn J. Keogh
2021ICDEFastDTW is approximate and Generally Slower than the Algorithm it Approximates (Extended Abstract).Renjie Wu, Eamonn J. Keogh
2021ICDMMatrix Profile XXIII: Contrast Profile: A Novel Time Series Primitive that Allows Real World Classification.Ryan Mercer, Sara Alaee, Alireza Abdoli, Shailendra Singh, Amy C. Murillo, Eamonn J. Keogh
2020ICDEMatrix Profile XVII: Indexing the Matrix Profile to Allow Arbitrary Range Queries.Yan Zhu, Chin-Chia Michael Yeh, Zachary Zimmerman, Eamonn J. Keogh
2020ICDMMatrix Profile XXII: Exact Discovery of Time Series Motifs under DTW.Sara Alaee, Kaveh Kamgar, Eamonn J. Keogh
2020ICDMMERLIN: Parameter-Free Discovery of Arbitrary Length Anomalies in Massive Time Series Archives.Takaaki Nakamura, Makoto Imamura, Ryan Mercer, Eamonn J. Keogh
2020KDDFitbit for Chickens?: Time Series Data Mining Can Increase the Productivity of Poultry Farms.Alireza Abdoli, Sara Alaee, Shima Imani, Amy C. Murillo, Alec C. Gerry, Leslie Hickle, Eamonn J. Keogh
2020KDDMatrix Profile XXI: A Geometric Approach to Time Series Chains Improves Robustness.Makoto Imamura, Takaaki Nakamura, Eamonn J. Keogh
2020WWWNatura: Towards Conversational Analytics for Comparing and Contrasting Time Series.Shima Imani, Eamonn J. Keogh
2020SDMFeatures or Shape? Tackling the False Dichotomy of Time Series Classification.Sara Alaee, Alireza Abdoli, Christian R. Shelton, Amy C. Murillo, Alec C. Gerry, Eamonn J. Keogh
2019CLOUDMatrix Profile XIV: Scaling Time Series Motif Discovery with GPUs to Break a Quintillion Pairwise Comparisons a Day and Beyond.Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth J. Funning, Philip Brisk, Eamonn J. Keogh
2019DSAAMatrix Profile XVI: Efficient and Effective Labeling of Massive Time Series Archives.Frank Madrid, Shailendra Singh, Quentin Chesnais, Kerry Mauck, Eamonn J. Keogh
2019ICDMMatrix Profile XIX: Time Series Semantic Motifs: A New Primitive for Finding Higher-Level Structure in Time Series.Shima Imani, Eamonn J. Keogh
2019ICDMMatrix Profile XV: Exploiting Time Series Consensus Motifs to Find Structure in Time Series Sets.Kaveh Kamgar, Shaghayegh Gharghabi, Eamonn J. Keogh
2019ICDMMatrix Profile XVIII: Time Series Mining in the Face of Fast Moving Streams using a Learned Approximate Matrix Profile.Zachary Zimmerman, Nader Shakibay Senobari, Gareth J. Funning, Evangelos E. Papalexakis, Samet Oymak, Philip Brisk, Eamonn J. Keogh
2019KDDOnline Amnestic DTW to allow Real-Time Golden Batch Monitoring.Chin-Chia Michael Yeh, Yan Zhu, Hoang Anh Dau, Amirali Darvishzadeh, Mikhail Noskov, Eamonn J. Keogh
2019WWWPutting the Human in the Time Series Analytics Loop.Shima Imani, Sara Alaee, Eamonn J. Keogh
2018ICDEGeneralized Dynamic Time Warping: Unleashing the Warping Power Hidden in Point-Wise Distances.Rodica Neamtu, Ramoza Ahsan, Elke A. Rundensteiner, Gbor N. Srkzy, Eamonn J. Keogh, Hoang Anh Dau, Cuong Nguyen, Charles Lovering
2018ICDMMatrix Profile XI: SCRIMP++: Time Series Motif Discovery at Interactive Speeds.Yan Zhu, Chin-Chia Michael Yeh, Zachary Zimmerman, Kaveh Kamgar, Eamonn J. Keogh
2018ICDMMatrix Profile XII: MPdist: A Novel Time Series Distance Measure to Allow Data Mining in More Challenging Scenarios.Shaghayegh Gharghabi, Shima Imani, Anthony J. Bagnall, Amirali Darvishzadeh, Eamonn J. Keogh
2018ICMLATime Series Classification to Improve Poultry Welfare.Alireza Abdoli, Amy C. Murillo, Chin-Chia Michael Yeh, Alec C. Gerry, Eamonn J. Keogh
2018IJCAITime Series Chains: A Novel Tool for Time Series Data Mining.Yan Zhu, Makoto Imamura, Daniel Nikovski, Eamonn J. Keogh
2018SIGMODMatrix Profile X: VALMOD - Scalable Discovery of Variable-Length Motifs in Data Series.Michele Linardi, Yan Zhu, Themis Palpanas, Eamonn J. Keogh
2018SIGMODVALMOD: A Suite for Easy and Exact Detection of Variable Length Motifs in Data Series.Michele Linardi, Yan Zhu, Themis Palpanas, Eamonn J. Keogh
2018SDMAccelerating Time Series Searching with Large Uniform Scaling.Yilin Shen, Yanping Chen, Eamonn J. Keogh, Hongxia Jin
2017ICDESearching Time Series with Invariance to Large Amounts of Uniform Scaling.Yilin Shen, Yanping Chen, Eamonn J. Keogh, Hongxia Jin
2017ICDMGenerating Synthetic Time Series to Augment Sparse Datasets.Germain Forestier, Franois Petitjean, Hoang Anh Dau, Geoffrey I. Webb, Eamonn J. Keogh
2017ICDMMatrix Profile VIII: Domain Agnostic Online Semantic Segmentation at Superhuman Performance Levels.Shaghayegh Gharghabi, Yifei Ding, Chin-Chia Michael Yeh, Kaveh Kamgar, Liudmila Ulanova, Eamonn J. Keogh
2017ICDMMatrix Profile VI: Meaningful Multidimensional Motif Discovery.Chin-Chia Michael Yeh, Nickolas Kavantzas, Eamonn J. Keogh
2017ICDMMatrix Profile VII: Time Series Chains: A New Primitive for Time Series Data Mining (Best Student Paper Award).Yan Zhu, Makoto Imamura, Daniel Nikovski, Eamonn J. Keogh
2017KDDMatrix Profile V: A Generic Technique to Incorporate Domain Knowledge into Motif Discovery.Hoang Anh Dau, Eamonn J. Keogh
2017SSDBMQuery Suggestion to allow Intuitive Interactive Search in Multidimensional Time Series.Yifei Ding, Eamonn J. Keogh
2016CIKMSemi-Supervision Dramatically Improves Time Series Clustering under Dynamic Time Warping.Hoang Anh Dau, Nurjahan Begum, Eamonn J. Keogh
2016ICDMPrefix and Suffix Invariant Dynamic Time Warping.Diego Furtado Silva, Gustavo E. A. P. A. Batista, Eamonn J. Keogh
2016ICDMMatrix Profile III: The Matrix Profile Allows Visualization of Salient Subsequences in Massive Time Series.Chin-Chia Michael Yeh, Helga Van Herle, Eamonn J. Keogh
2016ICDMMatrix Profile I: All Pairs Similarity Joins for Time Series: A Unifying View That Includes Motifs, Discords and Shapelets.Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, Eamonn J. Keogh
2016ICDMMatrix Profile II: Exploiting a Novel Algorithm and GPUs to Break the One Hundred Million Barrier for Time Series Motifs and Joins.Yan Zhu, Zachary Zimmerman, Nader Shakibay Senobari, Chin-Chia Michael Yeh, Gareth J. Funning, Abdullah Mueen, Philip Brisk, Eamonn J. Keogh
2016KDDExtracting Optimal Performance from Dynamic Time Warping.Abdullah Mueen, Eamonn J. Keogh
2016SDMClustering in the Face of Fast Changing Streams.Liudmila Ulanova, Nurjahan Begum, Mohammad Shokoohi-Yekta, Eamonn J. Keogh
2015KDDAccelerating Dynamic Time Warping Clustering with a Novel Admissible Pruning Strategy.Nurjahan Begum, Liudmila Ulanova, Jun Wang, Eamonn J. Keogh
2015KDDDiscovery of Meaningful Rules in Time Series.Mohammad Shokoohi-Yekta, Yanping Chen, Bilson J. L. Campana, Bing Hu, Jesin Zakaria, Eamonn J. Keogh
2015KDDEfficient Long-Term Degradation Profiling in Time Series for Complex Physical Systems.Liudmila Ulanova, Tan Yan, Haifeng Chen, Guofei Jiang, Eamonn J. Keogh, Kai Zhang
2015SDMOn the Non-Trivial Generalization of Dynamic Time Warping to the Multi-Dimensional Case.Mohammad Shokoohi-Yekta, Jun Wang, Eamonn J. Keogh
2015SDMScalable Clustering of Time Series with U-Shapelets.Liudmila Ulanova, Nurjahan Begum, Eamonn J. Keogh
2014ACSSCAccelerating the dynamic time warping distance measure using logarithmetic arithmetic.Joseph Tarango, Eamonn J. Keogh, Philip Brisk
2014ICDMDynamic Time Warping Averaging of Time Series Allows Faster and More Accurate Classification.Franois Petitjean, Germain Forestier, Geoffrey I. Webb, Ann E. Nicholson, Yanping Chen, Eamonn J. Keogh
2014SISAPGenerating Synthetic Data to Allow Learning from a Single Exemplar per Class.Liudmila Ulanova, Yuan Hao, Eamonn J. Keogh
2013ICDARClustering of Symbols Using Minimal Description Length.Oben M. Tataw, Thanawin Rakthanmanon, Eamonn J. Keogh
2013ICDMClassification of Multi-dimensional Streaming Time Series by Weighting Each Classifier's Track Record.Bing Hu, Yanping Chen, Jesin Zakaria, Liudmila Ulanova, Eamonn J. Keogh
2013ICDMParameter-Free Audio Motif Discovery in Large Data Archives.Yuan Hao, Mohammad Shokoohi-Yekta, George Papageorgiou, Eamonn J. Keogh
2013ICMLAApplying Machine Learning and Audio Analysis Techniques to Insect Recognition in Intelligent Traps.Diego Furtado Silva, Vincius M. A. de Souza, Gustavo E. A. P. A. Batista, Eamonn J. Keogh, Daniel P. W. Ellis
2013IJCAIData Mining a Trillion Time Series Subsequences Under Dynamic Time Warping.Thanawin Rakthanmanon, Eamonn J. Keogh
2013IRITowards a minimum description length based stopping criterion for semi-supervised time series classification.Nurjahan Begum, Bing Hu, Thanawin Rakthanmanon, Eamonn J. Keogh
2013IRIA Minimum Description Length Technique for Semi-Supervised Time Series Classification.Nurjahan Begum, Bing Hu, Thanawin Rakthanmanon, Eamonn J. Keogh
2013KDDDTW-D: time series semi-supervised learning from a single example.Yanping Chen, Bing Hu, Eamonn J. Keogh, Gustavo E. A. P. A. Batista
2013KDDTowards never-ending learning from time series streams.Yuan Hao, Yanping Chen, Jesin Zakaria, Bing Hu, Thanawin Rakthanmanon, Eamonn J. Keogh
2013SDMTime Series Classification under More Realistic Assumptions.Bing Hu, Yanping Chen, Eamonn J. Keogh
2013SDMFast Shapelets: A Scalable Algorithm for Discovering Time Series Shapelets.Eamonn J. Keogh, Thanawin Rakthanmanon
2012CIKMDiversifying query results on semi-structured data.Mahbub Hasan, Abdullah Mueen, Vassilis J. Tsotras, Eamonn J. Keogh
2012ICDMClustering Time Series Using Unsupervised-Shapelets.Jesin Zakaria, Abdullah Mueen, Eamonn J. Keogh
2012KDDSearching and mining trillions of time series subsequences under dynamic time warping.Thanawin Rakthanmanon, Bilson J. L. Campana, Abdullah Mueen, Gustavo E. A. P. A. Batista, M. Brandon Westover, Qiang Zhu, Jesin Zakaria, Eamonn J. Keogh
2012SIGMODGetting your acceptance rate to 80%: a checklist for publishing.Eamonn J. Keogh
2012SDMMonitoring and Mining Insect Sounds in Visual Space.Yuan Hao, Bilson J. L. Campana, Eamonn J. Keogh
2012SDMImage Mining of Historical Manuscripts to Establish Provenance.Bing Hu, Thanawin Rakthanmanon, Bilson J. L. Campana, Abdullah Mueen, Eamonn J. Keogh
2012SDMMining Massive Archives of Mice Sounds with Symbolized Representations.Jesin Zakaria, Sarah Rotschafer, Abdullah Mueen, Khaleel Razak, Eamonn J. Keogh
2012SDMA Novel Approximation to Dynamic Time Warping allows Anytime Clustering of Massive Time Series Datasets.Qiang Zhu, Gustavo E. A. P. A. Batista, Thanawin Rakthanmanon, Eamonn J. Keogh
2011ICDARSearching historical manuscripts for near-duplicate figures.Thanawin Rakthanmanon, Qiang Zhu, Eamonn J. Keogh
2011ICDMDiscovering the Intrinsic Cardinality and Dimensionality of Time Series Using MDL.Bing Hu, Thanawin Rakthanmanon, Yuan Hao, Scott Evans, Stefano Lonardi, Eamonn J. Keogh
2011ICDMTime Series Epenthesis: Clustering Time Series Streams Requires Ignoring Some Data.Thanawin Rakthanmanon, Eamonn J. Keogh, Stefano Lonardi, Scott Evans
2011ICDMMining Historical Documents for Near-Duplicate Figures.Thanawin Rakthanmanon, Qiang Zhu, Eamonn J. Keogh
2011ICMLATowards Automatic Classification on Flying Insects Using Inexpensive Sensors.Gustavo E. A. P. A. Batista, Yuan Hao, Eamonn J. Keogh, Agenor Mafra-Neto
2011KDDSIGKDD demo: sensors and software to allow computational entomology, an emerging application of data mining.Gustavo E. A. P. A. Batista, Eamonn J. Keogh, Agenor Mafra-Neto, Edgar Rowton
2011KDDLogical-shapelets: an expressive primitive for time series classification.Abdullah Mueen, Eamonn J. Keogh, Neal E. Young
2011SDMA Complexity-Invariant Distance Measure for Time Series.Gustavo E. A. P. A. Batista, Xiaoyue Wang, Eamonn J. Keogh
2010ICDMiSAX 2.0: Indexing and Mining One Billion Time Series.Alessandro Camerra, Themis Palpanas, Jin Shieh, Eamonn J. Keogh
2010ICDMHow to Do Good Data Mining Research and Get it Published in Top Venues.Eamonn J. Keogh
2010ICDMData Editing Techniques to Allow the Application of Distance-Based Outlier Detection to Streams.Vit Niennattrakul, Eamonn J. Keogh, Chotirat Ann Ratanamahatana
2010ICDMAccelerating Dynamic Time Warping Subsequence Search with GPUs and FPGAs.Doruk Sart, Abdullah Mueen, Walid A. Najjar, Eamonn J. Keogh, Vit Niennattrakul
2010ICDMPolishing the Right Apple: Anytime Classification Also Benefits Data Streams with Constant Arrival Times.Jin Shieh, Eamonn J. Keogh
2010ICDMMother Fugger: Mining Historical Manuscripts with Local Color Patches.Qiang Zhu, Eamonn J. Keogh
2010ICMLAClassification of Live Moths Combining Texture, Color and Shape Primitives.Gustavo E. A. P. A. Batista, Bilson J. L. Campana, Eamonn J. Keogh
2010IDAUsing CAPTCHAs to Index Cultural Artifacts.Qiang Zhu, Eamonn J. Keogh
2010KDDOnline discovery and maintenance of time series motifs.Abdullah Mueen, Eamonn J. Keogh
2010SDMA Compression Based Distance Measure for Texture.Bilson J. L. Campana, Eamonn J. Keogh
2009ICDMFinding Time Series Motifs in Disk-Resident Data.Abdullah Mueen, Eamonn J. Keogh, Nima Bigdely Shamlo
2009ISMAugmenting Historical Manuscripts with Automatic Hyperlinks.Xiaoyue Wang, Eamonn J. Keogh
2009KDDTime series shapelets: a new primitive for data mining.Lexiang Ye, Eamonn J. Keogh
2009KDDAugmenting the generalized hough transform to enable the mining of petroglyphs.Qiang Zhu, Xiaoyue Wang, Eamonn J. Keogh, Sang-Hee Lee
2009SDMExact Discovery of Time Series Motifs.Abdullah Mueen, Eamonn J. Keogh, Qiang Zhu, Sydney Cash, M. Brandon Westover
2009SDMAutocannibalistic and Anyspace Indexing Algorithms with Application to Sensor Data Mining.Lexiang Ye, Xiaoyue Wang, Eamonn J. Keogh, Agenor Mafra-Neto
2008ICTAIReal-Time Classification of Streaming Sensor Data.Shashwati Kasetty, Candice Stafford, Gregory P. Walker, Xiaoyue Wang, Eamonn J. Keogh
2008KDDUntitled recordJin Shieh, Eamonn J. Keogh
2008SDMThe Asymmetric Approximate Anytime Join: A New Primitive with Applications to Data Mining.Lexiang Ye, Xiaoyue Wang, Dragomir Yankov, Eamonn J. Keogh
2007ICDMLocally Constrained Support Vector Clustering.Dragomir Yankov, Eamonn J. Keogh, Kin Fai Kan
2007ICDMDisk Aware Discord Discovery: Finding Unusual Time Series in Terabyte Sized Datasets.Dragomir Yankov, Eamonn J. Keogh, Umaa Rebbapragada
2007KDDDetecting time series motifs under uniform scaling.Dragomir Yankov, Eamonn J. Keogh, Jose Medina, Bill Yuan-chi Chiu, Victor B. Zordan
2007SDMWAT: Finding Top-K Discords in Time Series Database.Yingyi Bu, Oscar Tat-Wing Leung, Ada Wai-Chee Fu, Eamonn J. Keogh, Jian Pei, Sam Meshkin
2007SDMFinding Motifs in a Database of Shapes.Xiaopeng Xi, Eamonn J. Keogh, Li Wei, Agenor Mafra-Neto
2007SDMFast Best-Match Shape Searching in Rotation Invariant Metric Spaces.Dragomir Yankov, Eamonn J. Keogh, Li Wei, Xiaopeng Xi, Wendy L. Hodges
2006ADMAFinding Time Series Discords Based on Haar Transform.Ada Wai-Chee Fu, Oscar Tat-Wing Leung, Eamonn J. Keogh, Jessica Lin
2006DACConfigurable cache subsetting for fast cache tuning.Pablo Viana, Ann Gordon-Ross, Eamonn J. Keogh, Edna Barros, Frank Vahid
2006ICDMIntelligent Icons: Integrating Lite-Weight Data Mining and Visualization into GUI Operating Systems.Eamonn J. Keogh, Li Wei, Xiaopeng Xi, Stefano Lonardi, Jin Shieh, Scott Sirowy
2006ICDMAnytime Classification Using the Nearest Neighbor Algorithm with Applications to Stream Mining.Ken Ueno, Xiaopeng Xi, Eamonn J. Keogh, Dah-Jye Lee
2006ICDMClustering Workflow Requirements Using Compression Dissimilarity Measure.Li Wei, John C. Handley, Nathaniel Martin, Tong Sun, Eamonn J. Keogh
2006ICDMSAXually Explicit Images: Finding Unusual Shapes.Li Wei, Eamonn J. Keogh, Xiaopeng Xi
2006ICDMManifold Clustering of Shapes.Dragomir Yankov, Eamonn J. Keogh
2006ICMLFast time series classification using numerosity reduction.Xiaopeng Xi, Eamonn J. Keogh, Christian R. Shelton, Li Wei, Chotirat Ann Ratanamahatana
2006KDDGlobal distance-based segmentation of trajectories.Aris Anagnostopoulos, Michail Vlachos, Marios Hadjieleftheriou, Eamonn J. Keogh, Philip S. Yu
2006KDDSemi-supervised time series classification.Li Wei, Eamonn J. Keogh
2006VLDBA Decade of Progress in Indexing and Mining Large Time Series Databases.Eamonn J. Keogh
2006VLDBLB_Keogh Supports Exact Indexing of Shapes under Rotation Invariance with Arbitrary Representations and Distance Measures.Eamonn J. Keogh, Li Wei, Xiaopeng Xi, Sang-Hee Lee, Michail Vlachos
2005CBMSApproximations to Magic: Finding Unusual Medical Time Series.Jessica Lin, Eamonn J. Keogh, Ada Wai-Chee Fu, Helga Van Herle
2005CBMSA Practical Tool for Visualizing and Data Mining Medical Time Series.Li Wei, Nitin Kumar, Venkata Nishanth Lolla, Eamonn J. Keogh, Stefano Lonardi, Chotirat (Ann) Ratanamahatana, Helga Van Herle
2005ICDMHOT SAX: Efficiently Finding the Most Unusual Time Series Subsequence.Eamonn J. Keogh, Jessica Lin, Ada Wai-Chee Fu
2005ICDMPartial Elastic Matching of Time Series.Longin Jan Latecki, Vasileios Megalooikonomou, Qiang Wang, Rolf Lakmper, Chotirat (Ann) Ratanamahatana, Eamonn J. Keogh
2005ICDMAtomic Wedgie: Efficient Query Filtering for Streaming Times Series.Li Wei, Eamonn J. Keogh, Helga Van Herle, Agenor Mafra-Neto
2005ICTAIDot Plots for Time Series Analysis.Dragomir Yankov, Eamonn J. Keogh, Stefano Lonardi, Ada Wai-Chee Fu
2005KESUsing Relevance Feedback to Learn Both the Distance Measure and the Query in Multimedia Databases.Chotirat (Ann) Ratanamahatana, Eamonn J. Keogh
2005MDMEfficient trajectory joins using symbolic representations.Petko Bakalov, Marios Hadjieleftheriou, Eamonn J. Keogh, Vassilis J. Tsotras
2005PAKDDA MPAA-Based Iterative Clustering Algorithm Augmented by Nearest Neighbors Search for Time-Series Data Streams.Jessica Lin, Michail Vlachos, Eamonn J. Keogh, Dimitrios Gunopulos, Jian-Wei Liu, Shou-Jian Yu, Jia-Jin Le
2005PAKDDA Novel Bit Level Time Series Representation with Implication of Similarity Search and Clustering.Chotirat (Ann) Ratanamahatana, Eamonn J. Keogh, Anthony J. Bagnall, Stefano Lonardi
2005VLDBScaling and Time Warping in Time Series Querying.Ada Wai-Chee Fu, Eamonn J. Keogh, Leo Yung Hang Lau, Chotirat (Ann) Ratanamahatana
2005SDMTime-series Bitmaps: a Practical Visualization Tool for Working with Large Time Series Databases.Nitin Kumar, Venkata Nishanth Lolla, Eamonn J. Keogh, Stefano Lonardi, Chotirat (Ann) Ratanamahatana
2005SDMThree Myths about Dynamic Time Warping Data Mining.Chotirat (Ann) Ratanamahatana, Eamonn J. Keogh
2005SSDBMAssumption-Free Anomaly Detection in Time Series.Li Wei, Nitin Kumar, Venkata Nishanth Lolla, Eamonn J. Keogh, Stefano Lonardi, Chotirat (Ann) Ratanamahatana
2004EDBTIterative Incremental Clustering of Time Series.Jessica Lin, Michail Vlachos, Eamonn J. Keogh, Dimitrios Gunopulos
2004ICDEOnline Amnesic Approximation of Streaming Time Series.Themistoklis Palpanas, Michail Vlachos, Eamonn J. Keogh, Dimitrios Gunopulos, Wagner Truppel
2004KDDTowards parameter-free data mining.Eamonn J. Keogh, Stefano Lonardi, Chotirat (Ann) Ratanamahatana
2004KDDVisually mining and monitoring massive time series.Jessica Lin, Eamonn J. Keogh, Stefano Lonardi, Jeffrey P. Lankford, Donna M. Nystrom
2004VLDBVizTree: a Tool for Visually Mining and Monitoring Massive Time Series Databases.Jessica Lin, Eamonn J. Keogh, Stefano Lonardi, Jeffrey P. Lankford, Donna M. Nystrom
2004VLDBIndexing Large Human-Motion Databases.Eamonn J. Keogh, Themis Palpanas, Victor B. Zordan, Dimitrios Gunopulos, Marc Cardle
2004SDMMaking Time-Series Classification More Accurate Using Learned Constraints.Chotirat (Ann) Ratanamahatana, Eamonn J. Keogh
2003ICDMClustering of Time Series Subsequences is Meaningless: Implications for Previous and Future Research.Eamonn J. Keogh, Jessica Lin, Wagner Truppel
2003IDEALGrid-Based Indexing for Large Time Series Databases.Jiyuan An, Hanxiong Chen, Kazutaka Furuse, Nobuo Ohbo, Eamonn J. Keogh
2003KDDProbabilistic discovery of time series motifs.Bill Yuan-chi Chiu, Eamonn J. Keogh, Stefano Lonardi
2003KDDIndexing multi-dimensional time-series with support for multiple distance measures.Michail Vlachos, Marios Hadjieleftheriou, Dimitrios Gunopulos, Eamonn J. Keogh
2002FQASAn Augmented Visual Query Mechanism for Finding Patterns in Time Series Data.Eamonn J. Keogh, Harry Hochheiser, Ben Shneiderman
2002ICDMMining Motifs in Massive Time Series Databases.Pranav Patel, Eamonn J. Keogh, Jessica Lin, Stefano Lonardi
2002KDDOn the need for time series data mining benchmarks: a survey and empirical demonstration.Eamonn J. Keogh, Shruti Kasetty
2002KDDFinding surprising patterns in a time series database in linear time and space.Eamonn J. Keogh, Stefano Lonardi, Bill Yuan-chi Chiu
2002VLDBExact Indexing of Dynamic Time Warping.Eamonn J. Keogh
2002SDMIterative Deepening Dynamic Time Warping for Time Series.Selina Chu, Eamonn J. Keogh, David M. Hart, Michael J. Pazzani
2001ICDMAn Online Algorithm for Segmenting Time Series.Eamonn J. Keogh, Selina Chu, David M. Hart, Michael J. Pazzani
2001KDDEnsemble-index: a new approach to indexing large databases.Eamonn J. Keogh, Selina Chu, Michael J. Pazzani
2001SIGMODLocally Adaptive Dimensionality Reduction for Indexing Large Time Series Databases.Eamonn J. Keogh, Kaushik Chakrabarti, Sharad Mehrotra, Michael J. Pazzani
2001SDMDerivative Dynamic Time Warping.Eamonn J. Keogh, Michael J. Pazzani
2000KDDScaling up dynamic time warping for datamining applications.Eamonn J. Keogh, Michael J. Pazzani
2000PAKDDA Simple Dimensionality Reduction Technique for Fast Similarity Search in Large Time Series Databases.Eamonn J. Keogh, Michael J. Pazzani
1999AISTATSLearning augmented Bayesian classifiers: A comparison of distribution-based and classification-based approaches.Eamonn J. Keogh, Michael J. Pazzani
1999SIGIRRelevance Feedback Retrieval of Time Series Data.Eamonn J. Keogh, Michael J. Pazzani
1999SSDBMAn Indexing Scheme for Fast Similarity Search in Large Time Series Databases.Eamonn J. Keogh, Michael J. Pazzani
1998KDDAn Enhanced Representation of Time Series Which Allows Fast and Accurate Classification, Clustering and Relevance Feedback.Eamonn J. Keogh, Michael J. Pazzani
1997ICTAIFast Similarity Search in the Presence of Longitudinal Scaling in Time Series Databases.Eamonn J. Keogh
1997KDDA Probabilistic Approach to Fast Pattern Matching in Time Series Databases.Eamonn J. Keogh, Padhraic Smyth