Eamonn J. Keogh

According to our database, Eamonn J. Keogh authored at least 294 papers between 1997 and 2024.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of two.

2024

Novelets: a new primitive that allows online detection of emerging behaviors in time series.

Ryan Mercer, Eamonn J. Keogh

Knowl. Inf. Syst., January, 2024

PUPAE: 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

CoRR, 2024

2023

Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of Progress.

Renjie Wu, Eamonn J. Keogh

IEEE Trans. Knowl. Data Eng., March, 2023

When is Early Classification of Time Series Meaningful?

Renjie Wu, Audrey Der, Eamonn J. Keogh

IEEE Trans. Knowl. Data Eng., March, 2023

MERLIN++: parameter-free discovery of time series anomalies.

Takaaki Nakamura, Ryan Mercer, Makoto Imamura, Eamonn J. Keogh

Data Min. Knowl. Discov., March, 2023

DAMP: accurate time series anomaly detection on trillions of datapoints and ultra-fast arriving data streams.

Yue Lu, Renjie Wu, Abdullah Mueen, Maria A. Zuluaga, Eamonn J. Keogh

Data Min. Knowl. Discov., March, 2023

Time Series Data Mining: A Unifying View.

Eamonn J. Keogh

Proc. VLDB Endow., 2023

Matrix Profile XXVIII: Discovering Multi-Dimensional Time Series Anomalies with <i>K</i> of <i>N</i> Anomaly Detection^†.

Sadaf Tafazoli, Eamonn J. Keogh

Proceedings of the 2023 SIAM International Conference on Data Mining, 2023

Getting an h-Index of 100 in 20 Years or Less!

Eamonn J. Keogh

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

Tailgating Behavior Detection On Rear Vehicles.

Seyhan Ucar, Ryan Mercer, Eamonn J. Keogh

Proceedings of the 25th IEEE International Conference on Intelligent Transportation Systems, 2023

Matrix Profile XXIX: C²²MP, Fusing catch 22 and the Matrix Profile to Produce an Efficient and Interpretable Anomaly Detector.

Sadaf Tafazoli, Yue Lu, Renjie Wu, Thirumalai Vinjamoor Akhil Srinivas, Hannah Dela Cruz, Ryan Mercer, Eamonn J. Keogh

Proceedings of the IEEE International Conference on Data Mining, 2023

Matrix 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

Proceedings of the IEEE International Conference on Data Mining, 2023

Feature Extraction Accelerator for Streaming Time Series.

Prithviraj Yuvaraj, Amin Akalantar, Eamonn J. Keogh, Philip Brisk

Proceedings of the 31st IEEE Annual International Symposium on Field-Programmable Custom Computing Machines, 2023

Sketching Multidimensional Time Series for Fast Discord Mining.

Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M. Phillips, Eamonn J. Keogh

Proceedings of the IEEE International Conference on Big Data, 2023

Ego-Network Transformer for Subsequence Classification in Time Series Data.

Chin-Chia Michael Yeh, Huiyuan Chen, Yujie Fan, Xin Dai, Yan Zheng, Vivian Lai, Junpeng Wang, Zhongfang Zhuang, Liang Wang, Wei Zhang, Eamonn J. Keogh

Proceedings of the IEEE International Conference on Big Data, 2023

Time Series Synthesis Using the Matrix Profile for Anonymization.

Audrey Der, Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Liang Wang, Wei Zhang, Eamonn J. Keogh

Proceedings of the IEEE International Conference on Big Data, 2023

2022

FastDTW is Approximate and Generally Slower Than the Algorithm it Approximates.

Renjie Wu, Eamonn J. Keogh

IEEE Trans. Knowl. Data Eng., 2022

Introducing the contrast profile: a novel time series primitive that allows real world classification.

Ryan Mercer, Sara Alaee, Alireza Abdoli, Nader Shakibay Senobari, Shailendra Singh, Amy C. Murillo, Eamonn J. Keogh

Data Min. Knowl. Discov., 2022

Error-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

Proceedings of the 2022 SIAM International Conference on Data Mining, 2022

Matrix 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

Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

Matrix Profile XXVI: Mplots: Scaling Time Series Similarity Matrices to Massive Data.

Maryam Shahcheraghi, Ryan Mercer, João Manuel De Almeida Rodrigues, Audrey Der, Hugo Filipe Silveira Gamboa, Zachary Zimmerman, Eamonn J. Keogh

Proceedings of the IEEE International Conference on Data Mining, 2022

Matrix Profile XXV: Introducing Novelets: A Primitive that Allows Online Detection of Emerging Behaviors in Time Series.

Ryan Mercer, Eamonn J. Keogh

Proceedings of the IEEE International Conference on Data Mining, 2022

Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of Progress (Extended Abstract).

Renjie Wu, Eamonn J. Keogh

Proceedings of the 38th IEEE International Conference on Data Engineering, 2022

When is Early Classification of Time Series Meaningful? (Extended Abstract).

Renjie Wu, Audrey Der, Eamonn J. Keogh

Proceedings of the 38th IEEE International Conference on Data Engineering, 2022

Matrix Profile XXVII: A Novel Distance Measure for Comparing Long Time Series.

Audrey Der, Chin-Chia Michael Yeh, Renjie Wu, Junpeng Wang, Yan Zheng, Zhongfang Zhuang, Liang Wang, Wei Zhang, Eamonn J. Keogh

Proceedings of the IEEE International Conference on Knowledge Graph, 2022

2021

Matrix Profile IX: Admissible Time Series Motif Discovery With Missing Data.

Yan Zhu, Abdullah Mueen, Eamonn J. Keogh

IEEE Trans. Knowl. Data Eng., 2021

Time series motifs discovery under DTW allows more robust discovery of conserved structure.

Sara Alaee, Ryan Mercer, Kaveh Kamgar, Eamonn J. Keogh

Data Min. Knowl. Discov., 2021

Matrix 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

Proceedings of the IEEE International Conference on Data Mining, 2021

FastDTW is approximate and Generally Slower than the Algorithm it Approximates (Extended Abstract).

Renjie Wu, Eamonn J. Keogh

Proceedings of the 37th IEEE International Conference on Data Engineering, 2021

Shape-Based Telemetry Approach for Distracted Driving Behavior Detection.

Ryan Mercer, Seyhan Ucar, Eamonn J. Keogh

Proceedings of the 2021 IEEE Conference on Standards for Communications and Networking, 2021

Matrix Profile Index Approximation for Streaming Time Series.

Maryam Shahcheraghi, Trevor Cappon, Samet Oymak, Evangelos E. Papalexakis, Eamonn J. Keogh, Zachary Zimmerman, Philip Brisk

Proceedings of the 2021 IEEE International Conference on Big Data (Big Data), 2021

2020

The Swiss army knife of time series data mining: ten useful things you can do with the matrix profile and ten lines of code.

Yan Zhu, Shaghayegh Gharghabi, Diego Furtado Silva, Hoang Anh Dau, Chin-Chia Michael Yeh, Nader Shakibay Senobari, Abdulaziz Almaslukh, Kaveh Kamgar, Zachary Zimmerman, Gareth J. Funning, Abdullah Mueen, Eamonn J. Keogh

Data Min. Knowl. Discov., 2020

Matrix profile goes MAD: variable-length motif and discord discovery in data series.

Michele Linardi, Yan Zhu, Themis Palpanas, Eamonn J. Keogh

Data Min. Knowl. Discov., 2020

Introducing time series snippets: a new primitive for summarizing long time series.

Shima Imani, Frank Madrid, Wei Ding, Scott E. Crouter, Eamonn J. Keogh

Data Min. Knowl. Discov., 2020

An ultra-fast time series distance measure to allow data mining in more complex real-world deployments.

Shaghayegh Gharghabi, Shima Imani, Anthony J. Bagnall, Amirali Darvishzadeh, Eamonn J. Keogh

Data Min. Knowl. Discov., 2020

Natura: Towards Conversational Analytics for Comparing and Contrasting Time Series.

Shima Imani, Eamonn J. Keogh

Proceedings of the Companion of The 2020 Web Conference 2020, 2020

Features 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

Proceedings of the 2020 SIAM International Conference on Data Mining, 2020

Matrix Profile XXI: A Geometric Approach to Time Series Chains Improves Robustness.

Makoto Imamura, Takaaki Nakamura, Eamonn J. Keogh

Proceedings of the KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2020

Fitbit 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

Proceedings of the KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2020

MERLIN: Parameter-Free Discovery of Arbitrary Length Anomalies in Massive Time Series Archives.

Takaaki Nakamura, Makoto Imamura, Ryan Mercer, Eamonn J. Keogh

Proceedings of the 20th IEEE International Conference on Data Mining, 2020

Matrix Profile XXII: Exact Discovery of Time Series Motifs under DTW.

Sara Alaee, Kaveh Kamgar, Eamonn J. Keogh

Proceedings of the 20th IEEE International Conference on Data Mining, 2020

Matrix Profile XVII: Indexing the Matrix Profile to Allow Arbitrary Range Queries.

Yan Zhu, Chin-Chia Michael Yeh, Zachary Zimmerman, Eamonn J. Keogh

Proceedings of the 36th IEEE International Conference on Data Engineering, 2020

2019

Fast Similarity Matrix Profile for Music Analysis and Exploration.

Diego Furtado Silva, Chin-Chia Michael Yeh, Yan Zhu, Gustavo E. A. P. A. Batista, Eamonn J. Keogh

IEEE Trans. Multim., 2019

Introducing time series chains: a new primitive for time series data mining.

Yan Zhu, Makoto Imamura, Daniel Nikovski, Eamonn J. Keogh

Knowl. Inf. Syst., 2019

The UCR time series archive.

Hoang Anh Dau, Anthony J. Bagnall, Kaveh Kamgar, Chin-Chia Michael Yeh, Yan Zhu, Shaghayegh Gharghabi, Chotirat Ann Ratanamahatana, Eamonn J. Keogh

IEEE CAA J. Autom. Sinica, 2019

Correction to: Domain agnostic online semantic segmentation for multi-dimensional time series.

Shaghayegh Gharghabi, Chin-Chia Michael Yeh, Yifei Ding, Wei Ding, Paul Hibbing, Samuel LaMunion, Andrew Kaplan, Scott E. Crouter, Eamonn J. Keogh

Data Min. Knowl. Discov., 2019

Domain agnostic online semantic segmentation for multi-dimensional time series.

Shaghayegh Gharghabi, Chin-Chia Michael Yeh, Yifei Ding, Wei Ding, Paul Hibbing, Samuel LaMunion, Andrew Kaplan, Scott E. Crouter, Eamonn J. Keogh

Data Min. Knowl. Discov., 2019

Time series classification for varying length series.

Chang Wei Tan, François Petitjean, Eamonn J. Keogh, Geoffrey I. Webb

CoRR, 2019

Putting the Human in the Time Series Analytics Loop.

Shima Imani, Sara Alaee, Eamonn J. Keogh

Proceedings of the Companion of The 2019 World Wide Web Conference, 2019

Online 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

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019

Matrix 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

Proceedings of the 2019 IEEE International Conference on Data Mining, 2019

Matrix Profile XV: Exploiting Time Series Consensus Motifs to Find Structure in Time Series Sets.

Kaveh Kamgar, Shaghayegh Gharghabi, Eamonn J. Keogh

Proceedings of the 2019 IEEE International Conference on Data Mining, 2019

Matrix Profile XIX: Time Series Semantic Motifs: A New Primitive for Finding Higher-Level Structure in Time Series.

Shima Imani, Eamonn J. Keogh

Proceedings of the 2019 IEEE International Conference on Data Mining, 2019

Matrix Profile XX: Finding and Visualizing Time Series Motifs of All Lengths using the Matrix Profile.

Frank Madrid, Shima Imani, Ryan Mercer, Zachary Zimmerman, Nader Shakibay Senobari, Eamonn J. Keogh

Proceedings of the 2019 IEEE International Conference on Big Knowledge, 2019

Matrix Profile XVI: Efficient and Effective Labeling of Massive Time Series Archives.

Frank Madrid, Shailendra Singh, Quentin Chesnais, Kerry Mauck, Eamonn J. Keogh

Proceedings of the 2019 IEEE International Conference on Data Science and Advanced Analytics, 2019

Matrix 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

Proceedings of the ACM Symposium on Cloud Computing, SoCC 2019, 2019

Time Series Classification: Lessons Learned in the (Literal) Field while Studying Chicken Behavior.

Alireza Abdoli, Amy C. Murillo, Alec C. Gerry, Eamonn J. Keogh

Proceedings of the 2019 IEEE International Conference on Big Data (IEEE BigData), 2019

2018

Exploiting a novel algorithm and GPUs to break the ten quadrillion pairwise comparisons 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

Knowl. Inf. Syst., 2018

Time series joins, motifs, discords and shapelets: a unifying view that exploits the matrix profile.

Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Zachary Zimmerman, Diego Furtado Silva, Abdullah Mueen, Eamonn J. Keogh

Data Min. Knowl. Discov., 2018

Speeding up similarity search under dynamic time warping by pruning unpromising alignments.

Diego Furtado Silva, Rafael Giusti, Eamonn J. Keogh, Gustavo E. A. P. A. Batista

Data Min. Knowl. Discov., 2018

Optimizing dynamic time warping's window width for time series data mining applications.

Hoang Anh Dau, Diego Furtado Silva, François Petitjean, Germain Forestier, Anthony J. Bagnall, Abdullah Mueen, Eamonn J. Keogh

Data Min. Knowl. Discov., 2018

Representation Learning by Reconstructing Neighborhoods.

Chin-Chia Michael Yeh, Yan Zhu, Evangelos E. Papalexakis, Abdullah Mueen, Eamonn J. Keogh

CoRR, 2018

The UEA multivariate time series classification archive, 2018.

Anthony J. Bagnall, Hoang Anh Dau, Jason Lines, Michael Flynn, James Large, Aaron Bostrom, Paul Southam, Eamonn J. Keogh

CoRR, 2018

Admissible Time Series Motif Discovery with Missing Data.

Yan Zhu, Abdullah Mueen, Eamonn J. Keogh

CoRR, 2018

VALMOD: A Suite for Easy and Exact Detection of Variable Length Motifs in Data Series.

Michele Linardi, Yan Zhu, Themis Palpanas, Eamonn J. Keogh

Proceedings of the 2018 International Conference on Management of Data, 2018

Matrix Profile X: VALMOD - Scalable Discovery of Variable-Length Motifs in Data Series.

Michele Linardi, Yan Zhu, Themis Palpanas, Eamonn J. Keogh

Proceedings of the 2018 International Conference on Management of Data, 2018

Accelerating Time Series Searching with Large Uniform Scaling.

Yilin Shen, Yanping Chen, Eamonn J. Keogh, Hongxia Jin

Proceedings of the 2018 SIAM International Conference on Data Mining, 2018

Time Series Chains: A Novel Tool for Time Series Data Mining.

Yan Zhu, Makoto Imamura, Daniel Nikovski, Eamonn J. Keogh

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018

Time Series Classification to Improve Poultry Welfare.

Alireza Abdoli, Amy C. Murillo, Chin-Chia Michael Yeh, Alec C. Gerry, Eamonn J. Keogh

Proceedings of the 17th IEEE International Conference on Machine Learning and Applications, 2018

Matrix 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

Proceedings of the IEEE International Conference on Data Mining, 2018

Matrix Profile XI: SCRIMP++: Time Series Motif Discovery at Interactive Speeds.

Yan Zhu, Chin-Chia Michael Yeh, Zachary Zimmerman, Kaveh Kamgar, Eamonn J. Keogh

Proceedings of the IEEE International Conference on Data Mining, 2018

Generalized Dynamic Time Warping: Unleashing the Warping Power Hidden in Point-Wise Distances.

Rodica Neamtu, Ramoza Ahsan, Elke A. Rundensteiner, Gábor N. Sárközy, Eamonn J. Keogh, Hoang Anh Dau, Cuong Nguyen, Charles Lovering

Proceedings of the 34th IEEE International Conference on Data Engineering, 2018

Matrix Profile XIII: Time Series Snippets: A New Primitive for Time Series Data Mining.

Shima Imani, Frank Madrid, Wei Ding, Scott E. Crouter, Eamonn J. Keogh

Proceedings of the 2018 IEEE International Conference on Big Knowledge, 2018

2017

Curse of Dimensionality.

Eamonn J. Keogh, Abdullah Mueen

Proceedings of the Encyclopedia of Machine Learning and Data Mining, 2017

Time Series.

Eamonn J. Keogh

Proceedings of the Encyclopedia of Machine Learning and Data Mining, 2017

Nearest Neighbor.

Eamonn J. Keogh

Proceedings of the Encyclopedia of Machine Learning and Data Mining, 2017

Instance-Based Learning.

Eamonn J. Keogh

Proceedings of the Encyclopedia of Machine Learning and Data Mining, 2017

Indexing and Mining Time Series Data.

Eamonn J. Keogh

Proceedings of the Encyclopedia of GIS., 2017

Matrix Profile IV: Using Weakly Labeled Time Series to Predict Outcomes.

Chin-Chia Michael Yeh, Nickolas Kavantzas, Eamonn J. Keogh

Proc. VLDB Endow., 2017

Generalizing DTW to the multi-dimensional case requires an adaptive approach.

Mohammad Shokoohi-Yekta, Bing Hu, Hongxia Jin, Jun Wang, Eamonn J. Keogh

Data Min. Knowl. Discov., 2017

Reliable early classification of time series based on discriminating the classes over time.

Usue Mori, Alexander Mendiburu, Eamonn J. Keogh, José Antonio Lozano

Data Min. Knowl. Discov., 2017

The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances.

Anthony J. Bagnall, Jason Lines, Aaron Bostrom, James Large, Eamonn J. Keogh

Data Min. Knowl. Discov., 2017

Query Suggestion to allow Intuitive Interactive Search in Multidimensional Time Series.

Yifei Ding, Eamonn J. Keogh

Proceedings of the 29th International Conference on Scientific and Statistical Database Management, 2017

Matrix Profile V: A Generic Technique to Incorporate Domain Knowledge into Motif Discovery.

Hoang Anh Dau, Eamonn J. Keogh

Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada, August 13, 2017

Matrix 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

Proceedings of the 2017 IEEE International Conference on Data Mining, 2017

Matrix Profile VI: Meaningful Multidimensional Motif Discovery.

Chin-Chia Michael Yeh, Nickolas Kavantzas, Eamonn J. Keogh

Proceedings of the 2017 IEEE International Conference on Data Mining, 2017

Matrix 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

Proceedings of the 2017 IEEE International Conference on Data Mining, 2017

Generating Synthetic Time Series to Augment Sparse Datasets.

Germain Forestier, François Petitjean, Hoang Anh Dau, Geoffrey I. Webb, Eamonn J. Keogh

Proceedings of the 2017 IEEE International Conference on Data Mining, 2017

Searching Time Series with Invariance to Large Amounts of Uniform Scaling.

Yilin Shen, Yanping Chen, Eamonn J. Keogh, Hongxia Jin

Proceedings of the 33rd IEEE International Conference on Data Engineering, 2017

Judicious setting of Dynamic Time Warping's window width allows more accurate classification of time series.

Hoang Anh Dau, Diego Furtado Silva, François Petitjean, Germain Forestier, Anthony J. Bagnall, Eamonn J. Keogh

Proceedings of the 2017 IEEE International Conference on Big Data (IEEE BigData 2017), 2017

2016

Faster and more accurate classification of time series by exploiting a novel dynamic time warping averaging algorithm.

François Petitjean, Germain Forestier, Geoffrey I. Webb, Ann E. Nicholson, Yanping Chen, Eamonn J. Keogh

Knowl. Inf. Syst., 2016

Irrevocable-choice algorithms for sampling from a stream.

Yan Zhu, Eamonn J. Keogh

Data Min. Knowl. Discov., 2016

Accelerating the discovery of unsupervised-shapelets.

Jesin Zakaria, Abdullah Mueen, Eamonn J. Keogh, Neal E. Young

Data Min. Knowl. Discov., 2016

Classification of streaming time series under more realistic assumptions.

Bing Hu, Yanping Chen, Eamonn J. Keogh

Data Min. Knowl. Discov., 2016

A General Framework for Density Based Time Series Clustering Exploiting a Novel Admissible Pruning Strategy.

Nurjahan Begum, Liudmila Ulanova, Hoang Anh Dau, Jun Wang, Eamonn J. Keogh

CoRR, 2016

Clustering in the Face of Fast Changing Streams.

Liudmila Ulanova, Nurjahan Begum, Mohammad Shokoohi-Yekta, Eamonn J. Keogh

Proceedings of the 2016 SIAM International Conference on Data Mining, 2016

Extracting Optimal Performance from Dynamic Time Warping.

Abdullah Mueen, Eamonn J. Keogh

Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016

SiMPle: Assessing Music Similarity Using Subsequences Joins.

Diego Furtado Silva, Chin-Chia Michael Yeh, Gustavo E. A. P. A. Batista, Eamonn J. Keogh

Proceedings of the 17th International Society for Music Information Retrieval Conference, 2016

Matrix 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

Proceedings of the IEEE 16th International Conference on Data Mining, 2016

Matrix 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

Proceedings of the IEEE 16th International Conference on Data Mining, 2016

Matrix Profile III: The Matrix Profile Allows Visualization of Salient Subsequences in Massive Time Series.

Chin-Chia Michael Yeh, Helga Van Herle, Eamonn J. Keogh

Proceedings of the IEEE 16th International Conference on Data Mining, 2016

Prefix and Suffix Invariant Dynamic Time Warping.

Diego Furtado Silva, Gustavo E. A. P. A. Batista, Eamonn J. Keogh

Proceedings of the IEEE 16th International Conference on Data Mining, 2016

Semi-Supervision Dramatically Improves Time Series Clustering under Dynamic Time Warping.

Hoang Anh Dau, Nurjahan Begum, Eamonn J. Keogh

Proceedings of the 25th ACM International Conference on Information and Knowledge Management, 2016

2015

Establishing the provenance of historical manuscripts with a novel distance measure.

Bing Hu, Thanawin Rakthanmanon, Bilson J. L. Campana, Abdullah Mueen, Eamonn J. Keogh

Pattern Anal. Appl., 2015

Exploring Low Cost Laser Sensors to Identify Flying Insect Species - Evaluation of Machine Learning and Signal Processing Methods.

Diego Furtado Silva, Vinícius M. A. de Souza, Daniel P. W. Ellis, Eamonn J. Keogh, Gustavo E. A. P. A. Batista

J. Intell. Robotic Syst., 2015

A general framework for never-ending learning from time series streams.

Yanping Chen, Yuan Hao, Thanawin Rakthanmanon, Jesin Zakaria, Bing Hu, Eamonn J. Keogh

Data Min. Knowl. Discov., 2015

Using the minimum description length to discover the intrinsic cardinality and dimensionality of time series.

Bing Hu, Thanawin Rakthanmanon, Yuan Hao, Scott Evans, Stefano Lonardi, Eamonn J. Keogh

Data Min. Knowl. Discov., 2015

Scalable Clustering of Time Series with U-Shapelets.

Liudmila Ulanova, Nurjahan Begum, Eamonn J. Keogh

Proceedings of the 2015 SIAM International Conference on Data Mining, Vancouver, BC, Canada, April 30, 2015

On the Non-Trivial Generalization of Dynamic Time Warping to the Multi-Dimensional Case.

Mohammad Shokoohi-Yekta, Jun Wang, Eamonn J. Keogh

Proceedings of the 2015 SIAM International Conference on Data Mining, Vancouver, BC, Canada, April 30, 2015

Efficient Long-Term Degradation Profiling in Time Series for Complex Physical Systems.

Liudmila Ulanova, Tan Yan, Haifeng Chen, Guofei Jiang, Eamonn J. Keogh, Kai Zhang

Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015

Discovery of Meaningful Rules in Time Series.

Mohammad Shokoohi-Yekta, Yanping Chen, Bilson J. L. Campana, Bing Hu, Jesin Zakaria, Eamonn J. Keogh

Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015

Accelerating Dynamic Time Warping Clustering with a Novel Admissible Pruning Strategy.

Nurjahan Begum, Liudmila Ulanova, Jun Wang, Eamonn J. Keogh

Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015

2014

Rare Time Series Motif Discovery from Unbounded Streams.

Nurjahan Begum, Eamonn J. Keogh

Proc. VLDB Endow., 2014

Beyond one billion time series: indexing and mining very large time series collections with i SAX2+.

Alessandro Camerra, Jin Shieh, Themis Palpanas, Thanawin Rakthanmanon, Eamonn J. Keogh

Knowl. Inf. Syst., 2014

CID: an efficient complexity-invariant distance for time series.

Gustavo E. A. P. A. Batista, Eamonn J. Keogh, Oben Moses Tataw, Vinícius M. A. de Souza

Data Min. Knowl. Discov., 2014

Flying Insect Classification with Inexpensive Sensors.

Yanping Chen, Adena Why, Gustavo E. A. P. A. Batista, Agenor Mafra-Neto, Eamonn J. Keogh

CoRR, 2014

Generating Synthetic Data to Allow Learning from a Single Exemplar per Class.

Liudmila Ulanova, Yuan Hao, Eamonn J. Keogh

Proceedings of the Similarity Search and Applications - 7th International Conference, 2014

Dynamic Time Warping Averaging of Time Series Allows Faster and More Accurate Classification.

François Petitjean, Germain Forestier, Geoffrey I. Webb, Ann E. Nicholson, Yanping Chen, Eamonn J. Keogh

Proceedings of the 2014 IEEE International Conference on Data Mining, 2014

Accelerating the dynamic time warping distance measure using logarithmetic arithmetic.

Joseph Tarango, Eamonn J. Keogh, Philip Brisk

Proceedings of the 48th Asilomar Conference on Signals, Systems and Computers, 2014

2013

Addressing Big Data Time Series: 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

ACM Trans. Knowl. Discov. Data, 2013

Quantitative Analysis of Live-Cell Growth at the Shoot Apex of Arabidopsis thaliana: Algorithms for Feature Measurement and Temporal Alignment.

Oben M. Tataw, G. Venugopala Reddy, Eamonn J. Keogh, Amit K. Roy-Chowdhury

IEEE ACM Trans. Comput. Biol. Bioinform., 2013

Experimental comparison of representation methods and distance measures for time series data.

Xiaoyue Wang, Abdullah Mueen, Hui Ding, Goce Trajcevski, Peter Scheuermann, Eamonn J. Keogh

Data Min. Knowl. Discov., 2013

Fast Shapelets: A Scalable Algorithm for Discovering Time Series Shapelets.

Eamonn J. Keogh, Thanawin Rakthanmanon

Proceedings of the 13th SIAM International Conference on Data Mining, 2013

Time Series Classification under More Realistic Assumptions.

Bing Hu, Yanping Chen, Eamonn J. Keogh

Proceedings of the 13th SIAM International Conference on Data Mining, 2013

Towards never-ending learning from time series streams.

Yuan Hao, Yanping Chen, Jesin Zakaria, Bing Hu, Thanawin Rakthanmanon, Eamonn J. Keogh

Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2013

DTW-D: time series semi-supervised learning from a single example.

Yanping Chen, Bing Hu, Eamonn J. Keogh, Gustavo E. A. P. A. Batista

Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2013

A Minimum Description Length Technique for Semi-Supervised Time Series Classification.

Nurjahan Begum, Bing Hu, Thanawin Rakthanmanon, Eamonn J. Keogh

Proceedings of the Integration of Reusable Systems [extended versions of the best papers which were presented at IEEE International Conference on Information Reuse and Integration and IEEE International Workshop on Formal Methods Integration, 2013

Towards a minimum description length based stopping criterion for semi-supervised time series classification.

Nurjahan Begum, Bing Hu, Thanawin Rakthanmanon, Eamonn J. Keogh

Proceedings of the IEEE 14th International Conference on Information Reuse & Integration, 2013

Data Mining a Trillion Time Series Subsequences Under Dynamic Time Warping.

Thanawin Rakthanmanon, Eamonn J. Keogh

Proceedings of the IJCAI 2013, 2013

Applying Machine Learning and Audio Analysis Techniques to Insect Recognition in Intelligent Traps.

Diego Furtado Silva, Vinícius M. A. de Souza, Gustavo E. A. P. A. Batista, Eamonn J. Keogh, Daniel P. W. Ellis

Proceedings of the 12th International Conference on Machine Learning and Applications, 2013

Parameter-Free Audio Motif Discovery in Large Data Archives.

Yuan Hao, Mohammad Shokoohi-Yekta, George Papageorgiou, Eamonn J. Keogh

Proceedings of the 2013 IEEE 13th International Conference on Data Mining, 2013

Classification of Multi-dimensional Streaming Time Series by Weighting Each Classifier's Track Record.

Bing Hu, Yanping Chen, Jesin Zakaria, Liudmila Ulanova, Eamonn J. Keogh

Proceedings of the 2013 IEEE 13th International Conference on Data Mining, 2013

Clustering of Symbols Using Minimal Description Length.

Oben M. Tataw, Thanawin Rakthanmanon, Eamonn J. Keogh

Proceedings of the 12th International Conference on Document Analysis and Recognition, 2013

Instruction set extensions for Dynamic Time Warping.

Joseph Tarango, Eamonn J. Keogh, Philip Brisk

Proceedings of the International Conference on Hardware/Software Codesign and System Synthesis, 2013

2012

Mining historical manuscripts with local color patches.

Qiang Zhu, Eamonn J. Keogh

Knowl. Inf. Syst., 2012

MDL-based time series clustering.

Thanawin Rakthanmanon, Eamonn J. Keogh, Stefano Lonardi, Scott Evans

Knowl. Inf. Syst., 2012

Efficiently Finding Near Duplicate Figures in Archives of Historical Documents.

Thanawin Rakthanmanon, Qiang Zhu, Eamonn J. Keogh

J. Multim., 2012

Getting your acceptance rate to 80%: a checklist for publishing.

Eamonn J. Keogh

Proceedings of the ACM SIGMOD/PODS PhD Symposium 2012, Scottsdale, AZ, USA, May 20, 2012, 2012

A 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

Proceedings of the Twelfth SIAM International Conference on Data Mining, 2012

Mining Massive Archives of Mice Sounds with Symbolized Representations.

Jesin Zakaria, Sarah Rotschafer, Abdullah Mueen, Khaleel Razak, Eamonn J. Keogh

Proceedings of the Twelfth SIAM International Conference on Data Mining, 2012

Image Mining of Historical Manuscripts to Establish Provenance.

Bing Hu, Thanawin Rakthanmanon, Bilson J. L. Campana, Abdullah Mueen, Eamonn J. Keogh

Proceedings of the Twelfth SIAM International Conference on Data Mining, 2012

Monitoring and Mining Insect Sounds in Visual Space.

Yuan Hao, Bilson J. L. Campana, Eamonn J. Keogh

Proceedings of the Twelfth SIAM International Conference on Data Mining, 2012

Searching 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

Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2012

Clustering Time Series Using Unsupervised-Shapelets.

Jesin Zakaria, Abdullah Mueen, Eamonn J. Keogh

Proceedings of the 12th IEEE International Conference on Data Mining, 2012

Diversifying query results on semi-structured data.

Mahbub Hasan, Abdullah Mueen, Vassilis J. Tsotras, Eamonn J. Keogh

Proceedings of the 21st ACM International Conference on Information and Knowledge Management, 2012

2011

Data, Mining Time Series Data.

Eamonn J. Keogh

Proceedings of the International Encyclopedia of Statistical Science, 2011

An efficient and effective similarity measure to enable data mining of petroglyphs.

Qiang Zhu, Xiaoyue Wang, Eamonn J. Keogh, Sang-Hee Lee

Data Min. Knowl. Discov., 2011

Time series shapelets: a novel technique that allows accurate, interpretable and fast classification.

Lexiang Ye, Eamonn J. Keogh

Data Min. Knowl. Discov., 2011

A disk-aware algorithm for time series motif discovery.

Abdullah Mueen, Eamonn J. Keogh, Qiang Zhu, Sydney Cash, M. Brandon Westover, Nima Bigdely Shamlo

Data Min. Knowl. Discov., 2011

A Complexity-Invariant Distance Measure for Time Series.

Gustavo E. A. P. A. Batista, Xiaoyue Wang, Eamonn J. Keogh

Proceedings of the Eleventh SIAM International Conference on Data Mining, 2011

Logical-shapelets: an expressive primitive for time series classification.

Abdullah Mueen, Eamonn J. Keogh, Neal E. Young

Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2011

SIGKDD 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

Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2011

Towards Automatic Classification on Flying Insects Using Inexpensive Sensors.

Gustavo E. A. P. A. Batista, Yuan Hao, Eamonn J. Keogh, Agenor Mafra-Neto

Proceedings of the 10th International Conference on Machine Learning and Applications and Workshops, 2011

Mining Historical Documents for Near-Duplicate Figures.

Thanawin Rakthanmanon, Qiang Zhu, Eamonn J. Keogh

Proceedings of the 11th IEEE International Conference on Data Mining, 2011

Time Series Epenthesis: Clustering Time Series Streams Requires Ignoring Some Data.

Thanawin Rakthanmanon, Eamonn J. Keogh, Stefano Lonardi, Scott Evans

Proceedings of the 11th IEEE International Conference on Data Mining, 2011

Discovering the Intrinsic Cardinality and Dimensionality of Time Series Using MDL.

Bing Hu, Thanawin Rakthanmanon, Yuan Hao, Scott Evans, Stefano Lonardi, Eamonn J. Keogh

Proceedings of the 11th IEEE International Conference on Data Mining, 2011

Searching historical manuscripts for near-duplicate figures.

Thanawin Rakthanmanon, Qiang Zhu, Eamonn J. Keogh

Proceedings of the 2011 Workshop on Historical Document Imaging and Processing, 2011

Towards Discovering the Intrinsic Cardinality and Dimensionality of Time Series Using MDL.

Bing Hu, Thanawin Rakthanmanon, Yuan Hao, Scott Evans, Stefano Lonardi, Eamonn J. Keogh

Proceedings of the Algorithmic Probability and Friends. Bayesian Prediction and Artificial Intelligence, 2011

2010

Curse of Dimensionality.

Eamonn J. Keogh, Abdullah Mueen

Proceedings of the Encyclopedia of Machine Learning, 2010

Time Series.

Eamonn J. Keogh

Proceedings of the Encyclopedia of Machine Learning, 2010

Nearest Neighbor.

Eamonn J. Keogh

Proceedings of the Encyclopedia of Machine Learning, 2010

Instance-Based Learning.

Eamonn J. Keogh

Proceedings of the Encyclopedia of Machine Learning, 2010

Mining Time Series Data.

Chotirat Ann Ratanamahatana, Jessica Lin, Dimitrios Gunopulos, Eamonn J. Keogh, Michail Vlachos, Gautam Das

Proceedings of the Data Mining and Knowledge Discovery Handbook, 2nd ed., 2010

A brief survey on sequence classification.

Zhengzheng Xing, Jian Pei, Eamonn J. Keogh

SIGKDD Explor., 2010

A compression-based distance measure for texture.

Bilson J. L. Campana, Eamonn J. Keogh

Stat. Anal. Data Min., 2010

Annotating Historical Archives of Images.

Xiaoyue Wang, Lexiang Ye, Eamonn J. Keogh, Christian R. Shelton

Int. J. Digit. Libr. Syst., 2010

Experimental Comparison of Representation Methods and Distance Measures for Time Series Data

Xiaoyue Wang, Hui Ding, Goce Trajcevski, Peter Scheuermann, Eamonn J. Keogh

CoRR, 2010

Online discovery and maintenance of time series motifs.

Abdullah Mueen, Eamonn J. Keogh

Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2010

Using CAPTCHAs to Index Cultural Artifacts.

Qiang Zhu, Eamonn J. Keogh

Proceedings of the Advances in Intelligent Data Analysis IX, 9th International Symposium, 2010

Classification of Live Moths Combining Texture, Color and Shape Primitives.

Gustavo E. A. P. A. Batista, Bilson J. L. Campana, Eamonn J. Keogh

Proceedings of the Ninth International Conference on Machine Learning and Applications, 2010

Mother Fugger: Mining Historical Manuscripts with Local Color Patches.

Qiang Zhu, Eamonn J. Keogh

Proceedings of the ICDM 2010, 2010

Polishing the Right Apple: Anytime Classification Also Benefits Data Streams with Constant Arrival Times.

Jin Shieh, Eamonn J. Keogh

Proceedings of the ICDM 2010, 2010

Accelerating Dynamic Time Warping Subsequence Search with GPUs and FPGAs.

Doruk Sart, Abdullah Mueen, Walid A. Najjar, Eamonn J. Keogh, Vit Niennattrakul

Proceedings of the ICDM 2010, 2010

Data Editing Techniques to Allow the Application of Distance-Based Outlier Detection to Streams.

Vit Niennattrakul, Eamonn J. Keogh, Chotirat Ann Ratanamahatana

Proceedings of the ICDM 2010, 2010

How to Do Good Data Mining Research and Get it Published in Top Venues.

Eamonn J. Keogh

Proceedings of the ICDM 2010, 2010

iSAX 2.0: Indexing and Mining One Billion Time Series.

Alessandro Camerra, Themis Palpanas, Jin Shieh, Eamonn J. Keogh

Proceedings of the ICDM 2010, 2010

2009

Compression-Based Data Mining.

Eamonn J. Keogh, Li Keogh, John C. Handley

Proceedings of the Encyclopedia of Data Warehousing and Mining, Second Edition (4 Volumes), 2009

Supporting exact indexing of arbitrarily rotated shapes and periodic time series under Euclidean and warping distance measures.

Eamonn J. Keogh, Li Wei, Xiaopeng Xi, Michail Vlachos, Sang-Hee Lee, Pavlos Protopapas

VLDB J., 2009

<i>i</i>SAX: disk-aware mining and indexing of massive time series datasets.

Jin Shieh, Eamonn J. Keogh

Data Min. Knowl. Discov., 2009

Autocannibalistic and Anyspace Indexing Algorithms with Application to Sensor Data Mining.

Lexiang Ye, Xiaoyue Wang, Eamonn J. Keogh, Agenor Mafra-Neto

Proceedings of the SIAM International Conference on Data Mining, 2009

Exact Discovery of Time Series Motifs.

Abdullah Mueen, Eamonn J. Keogh, Qiang Zhu, Sydney Cash, M. Brandon Westover

Proceedings of the SIAM International Conference on Data Mining, 2009

Augmenting the generalized hough transform to enable the mining of petroglyphs.

Qiang Zhu, Xiaoyue Wang, Eamonn J. Keogh, Sang-Hee Lee

Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Paris, France, June 28, 2009

Time series shapelets: a new primitive for data mining.

Lexiang Ye, Eamonn J. Keogh

Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Paris, France, June 28, 2009

Finding centuries-old hyperlinks with a novel semi-supervised learning technique.

Xiaoyue Wang, Eamonn J. Keogh

Proceedings of the 2009 Joint International Conference on Digital Libraries, 2009

Augmenting Historical Manuscripts with Automatic Hyperlinks.

Xiaoyue Wang, Eamonn J. Keogh

Proceedings of the 11th IEEE International Symposium on Multimedia, 2009

Finding Time Series Motifs in Disk-Resident Data.

Abdullah Mueen, Eamonn J. Keogh, Nima Bigdely Shamlo

Proceedings of the ICDM 2009, 2009

2008

A Clustering Analysis for Target Group Identification by Locality in Motor Insurance Industry.

Xiaozhe Wang, Eamonn J. Keogh

Proceedings of the Soft Computing Applications in Business, 2008

Indexing and Mining Time Series Data.

Eamonn J. Keogh

Proceedings of the Encyclopedia of GIS., 2008

Scaling and time warping in time series querying.

Ada Wai-Chee Fu, Eamonn J. Keogh, Leo Yung Hang Lau, Chotirat (Ann) Ratanamahatana, Raymond Chi-Wing Wong

VLDB J., 2008

Fast Best-Match Shape Searching in Rotation-Invariant Metric Spaces.

Dragomir Yankov, Eamonn J. Keogh, Li Wei, Xiaopeng Xi, Wendy L. Hodges

IEEE Trans. Multim., 2008

Streaming Time Series Summarization Using User-Defined Amnesic Functions.

Themis Palpanas, Michail Vlachos, Eamonn J. Keogh, Dimitrios Gunopulos

IEEE Trans. Knowl. Data Eng., 2008

Querying and mining of time series data: experimental comparison of representations and distance measures.

Hui Ding, Goce Trajcevski, Peter Scheuermann, Xiaoyue Wang, Eamonn J. Keogh

Proc. VLDB Endow., 2008

Converting non-parametric distance-based classification to anytime algorithms.

Xiaopeng Xi, Ken Ueno, Eamonn J. Keogh, Dah-Jye Lee

Pattern Anal. Appl., 2008

Disk aware discord discovery: finding unusual time series in terabyte sized datasets.

Dragomir Yankov, Eamonn J. Keogh, Umaa Rebbapragada

Knowl. Inf. Syst., 2008

Efficiently finding unusual shapes in large image databases.

Li Wei, Eamonn J. Keogh, Xiaopeng Xi, Melissa Yoder

Data Min. Knowl. Discov., 2008

The Asymmetric Approximate Anytime Join: A New Primitive with Applications to Data Mining.

Lexiang Ye, Xiaoyue Wang, Dragomir Yankov, Eamonn J. Keogh

Proceedings of the SIAM International Conference on Data Mining, 2008

<i>i</i>SAX: indexing and mining terabyte sized time series.

Jin Shieh, Eamonn J. Keogh

Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2008

Real-Time Classification of Streaming Sensor Data.

Shashwati Kasetty, Candice Stafford, Gregory P. Walker, Xiaoyue Wang, Eamonn J. Keogh

Proceedings of the 20th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2008), 2008

2007

Efficient query filtering for streaming time series with applications to semisupervised learning of time series classifiers.

Li Wei, Eamonn J. Keogh, Helga Van Herle, Agenor Mafra-Neto, Russell J. Abbott

Knowl. Inf. Syst., 2007

Finding the most unusual time series subsequence: algorithms and applications.

Eamonn J. Keogh, Jessica Lin, Sang-Hee Lee, Helga Van Herle

Knowl. Inf. Syst., 2007

Domain-Driven, Actionable Knowledge Discovery.

Longbing Cao, Chengqi Zhang, Qiang Yang, David A. Bell, Michail Vlachos, Bahar Taneri, Eamonn J. Keogh, Philip S. Yu, Ning Zhong, Mafruz Zaman Ashrafi, David Taniar, Eugene Dubossarsky, Warwick Graco

IEEE Intell. Syst., 2007

Experiencing SAX: a novel symbolic representation of time series.

Jessica Lin, Eamonn J. Keogh, Li Wei, Stefano Lonardi

Data Min. Knowl. Discov., 2007

Compression-based data mining of sequential data.

Eamonn J. Keogh, Stefano Lonardi, Chotirat Ann Ratanamahatana, Li Wei, Sang-Hee Lee, John C. Handley

Data Min. Knowl. Discov., 2007

Finding Motifs in a Database of Shapes.

Xiaopeng Xi, Eamonn J. Keogh, Li Wei, Agenor Mafra-Neto

Proceedings of the Seventh SIAM International Conference on Data Mining, 2007

WAT: 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

Proceedings of the Seventh SIAM International Conference on Data Mining, 2007

Visual Exploration of Genomic Data.

Michail Vlachos, Bahar Taneri, Eamonn J. Keogh, Philip S. Yu

Proceedings of the Knowledge Discovery in Databases: PKDD 2007, 2007

Detecting time series motifs under uniform scaling.

Dragomir Yankov, Eamonn J. Keogh, Jose Medina, Bill Yuan-chi Chiu, Victor B. Zordan

Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2007

Locally Constrained Support Vector Clustering.

Dragomir Yankov, Eamonn J. Keogh, Kin Fai Kan

Proceedings of the 7th IEEE International Conference on Data Mining (ICDM 2007), 2007

TS2-tree - an efficient similarity based organization for trajectory data.

Petko Bakalov, Eamonn J. Keogh, Vassilis J. Tsotras

Proceedings of the 15th ACM International Symposium on Geographic Information Systems, 2007

2006

Indexing Multidimensional Time-Series.

Michail Vlachos, Marios Hadjieleftheriou, Dimitrios Gunopulos, Eamonn J. Keogh

VLDB J., 2006

Finding Unusual Medical Time-Series Subsequences: Algorithms and Applications.

Eamonn J. Keogh, Jessica Lin, Ada Wai-Chee Fu, Helga Van Herle

IEEE Trans. Inf. Technol. Biomed., 2006

Efficient Discovery of Unusual Patterns in Time Series.

Stefano Lonardi, Jessica Lin, Eamonn J. Keogh, Bill Yuan-chi Chiu

New Gener. Comput., 2006

A Bit Level Representation for Time Series Data Mining with Shape Based Similarity.

Anthony J. Bagnall, Chotirat (Ann) Ratanamahatana, Eamonn J. Keogh, Stefano Lonardi, Gareth J. Janacek

Data Min. Knowl. Discov., 2006

LB_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

Proceedings of the 32nd International Conference on Very Large Data Bases, 2006

A Decade of Progress in Indexing and Mining Large Time Series Databases.

Eamonn J. Keogh

Proceedings of the 32nd International Conference on Very Large Data Bases, 2006

Group SAX: Extending the Notion of Contrast Sets to Time Series and Multimedia Data.

Jessica Lin, Eamonn J. Keogh

Proceedings of the Knowledge Discovery in Databases: PKDD 2006, 2006

Data mining and information retrieval in time series/multimedia databases.

Eamonn J. Keogh

Proceedings of the 14th ACM International Conference on Multimedia, 2006

Semi-supervised time series classification.

Li Wei, Eamonn J. Keogh

Proceedings of the Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2006

Global distance-based segmentation of trajectories.

Aris Anagnostopoulos, Michail Vlachos, Marios Hadjieleftheriou, Eamonn J. Keogh, Philip S. Yu

Proceedings of the Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2006

Fast time series classification using numerosity reduction.

Xiaopeng Xi, Eamonn J. Keogh, Christian R. Shelton, Li Wei, Chotirat Ann Ratanamahatana

Proceedings of the Machine Learning, 2006

Manifold Clustering of Shapes.

Dragomir Yankov, Eamonn J. Keogh

Proceedings of the 6th IEEE International Conference on Data Mining (ICDM 2006), 2006

SAXually Explicit Images: Finding Unusual Shapes.

Li Wei, Eamonn J. Keogh, Xiaopeng Xi

Proceedings of the 6th IEEE International Conference on Data Mining (ICDM 2006), 2006

Clustering Workflow Requirements Using Compression Dissimilarity Measure.

Li Wei, John C. Handley, Nathaniel Martin, Tong Sun, Eamonn J. Keogh

Proceedings of the Workshops Proceedings of the 6th IEEE International Conference on Data Mining (ICDM 2006), 2006

Anytime Classification Using the Nearest Neighbor Algorithm with Applications to Stream Mining.

Ken Ueno, Xiaopeng Xi, Eamonn J. Keogh, Dah-Jye Lee

Proceedings of the 6th IEEE International Conference on Data Mining (ICDM 2006), 2006

Intelligent 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

Proceedings of the 6th IEEE International Conference on Data Mining (ICDM 2006), 2006

Ensembles of Nearest Neighbor Forecasts.

Dragomir Yankov, Dennis DeCoste, Eamonn J. Keogh

Proceedings of the Machine Learning: ECML 2006, 2006

Configurable cache subsetting for fast cache tuning.

Pablo Viana, Ann Gordon-Ross, Eamonn J. Keogh, Edna Barros, Frank Vahid

Proceedings of the 43rd Design Automation Conference, 2006

Finding Time Series Discords Based on Haar Transform.

Ada Wai-Chee Fu, Oscar Tat-Wing Leung, Eamonn J. Keogh, Jessica Lin

Proceedings of the Advanced Data Mining and Applications, Second International Conference, 2006

2005

Guest Editorial.

Eamonn J. Keogh

Mach. Learn., 2005

Exact indexing of dynamic time warping.

Eamonn J. Keogh, Chotirat (Ann) Ratanamahatana

Knowl. Inf. Syst., 2005

Clustering of time-series subsequences is meaningless: implications for previous and future research.

Eamonn J. Keogh, Jessica Lin

Knowl. Inf. Syst., 2005

Integrating Lite-Weight but Ubiquitous Data Mining into GUI Operating Systems.

Li Wei, Eamonn J. Keogh, Xiaopeng Xi, Stefano Lonardi

J. Univers. Comput. Sci., 2005

Visualizing and discovering non-trivial patterns in large time series databases.

Jessica Lin, Eamonn J. Keogh, Stefano Lonardi

Inf. Vis., 2005

Scaling and Time Warping in Time Series Querying.

Ada Wai-Chee Fu, Eamonn J. Keogh, Leo Yung Hang Lau, Chotirat (Ann) Ratanamahatana

Proceedings of the 31st International Conference on Very Large Data Bases, Trondheim, Norway, August 30, 2005

Visualization and Mining of Temporal Data.

Eamonn J. Keogh

Proceedings of the 16th IEEE Visualization Conference, 2005

Assumption-Free Anomaly Detection in Time Series.

Li Wei, Nitin Kumar, Venkata Nishanth Lolla, Eamonn J. Keogh, Stefano Lonardi, Chotirat (Ann) Ratanamahatana

Proceedings of the 17th International Conference on Scientific and Statistical Database Management, 2005

Three Myths about Dynamic Time Warping Data Mining.

Chotirat (Ann) Ratanamahatana, Eamonn J. Keogh

Proceedings of the 2005 SIAM International Conference on Data Mining, 2005

Time-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

Proceedings of the 2005 SIAM International Conference on Data Mining, 2005

Elastic Partial Matching of Time Series.

Longin Jan Latecki, Vasilis Megalooikonomou, Qiang Wang, Rolf Lakämper, Chotirat (Ann) Ratanamahatana, Eamonn J. Keogh

Proceedings of the Knowledge Discovery in Databases: PKDD 2005, 2005

Recent Advances in Mining Time Series Data.

Eamonn J. Keogh

Proceedings of the Knowledge Discovery in Databases: PKDD 2005, 2005

A Novel Bit Level Time Series Representation with Implication of Similarity Search and Clustering.

Chotirat (Ann) Ratanamahatana, Eamonn J. Keogh, Anthony J. Bagnall, Stefano Lonardi

Proceedings of the Advances in Knowledge Discovery and Data Mining, 2005

A 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

Proceedings of the Advances in Knowledge Discovery and Data Mining, 2005

Efficient trajectory joins using symbolic representations.

Petko Bakalov, Marios Hadjieleftheriou, Eamonn J. Keogh, Vassilis J. Tsotras

Proceedings of the 6th International Conference on Mobile Data Management (MDM 2005), 2005

Using Relevance Feedback to Learn Both the Distance Measure and the Query in Multimedia Databases.

Chotirat (Ann) Ratanamahatana, Eamonn J. Keogh

Proceedings of the Knowledge-Based Intelligent Information and Engineering Systems, 2005

Dot Plots for Time Series Analysis.

Dragomir Yankov, Eamonn J. Keogh, Stefano Lonardi, Ada Wai-Chee Fu

Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2005), 2005

Atomic Wedgie: Efficient Query Filtering for Streaming Times Series.

Li Wei, Eamonn J. Keogh, Helga Van Herle, Agenor Mafra-Neto

Proceedings of the 5th IEEE International Conference on Data Mining (ICDM 2005), 2005

Partial Elastic Matching of Time Series.

Longin Jan Latecki, Vasileios Megalooikonomou, Qiang Wang, Rolf Lakämper, Chotirat (Ann) Ratanamahatana, Eamonn J. Keogh

Proceedings of the 5th IEEE International Conference on Data Mining (ICDM 2005), 2005

HOT SAX: Efficiently Finding the Most Unusual Time Series Subsequence.

Eamonn J. Keogh, Jessica Lin, Ada Wai-Chee Fu

Proceedings of the 5th IEEE International Conference on Data Mining (ICDM 2005), 2005

Multimedia Retrieval Using Time Series Representation and Relevance Feedback.

Chotirat (Ann) Ratanamahatana, Eamonn J. Keogh

Proceedings of the Digital Libraries: Implementing Strategies and Sharing Experiences, 2005

A 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

Proceedings of the 18th IEEE Symposium on Computer-Based Medical Systems (CBMS 2005), 2005

Approximations to Magic: Finding Unusual Medical Time Series.

Jessica Lin, Eamonn J. Keogh, Ada Wai-Chee Fu, Helga Van Herle

Proceedings of the 18th IEEE Symposium on Computer-Based Medical Systems (CBMS 2005), 2005

Mining Time Series Data.

Chotirat (Ann) Ratanamahatana, Jessica Lin, Dimitrios Gunopulos, Eamonn J. Keogh, Michail Vlachos, Gautam Das

Proceedings of the Data Mining and Knowledge Discovery Handbook., 2005

Indexing Multi-Dimensional Trajectories for Similarity Queries.

Michail Vlachos, Marios Hadjieleftheriou, Eamonn J. Keogh, Dimitrios Gunopulos

Proceedings of the Spatial Databases: Technologies, Techniques and Trends, 2005

2004

A Grid-Based Index Method for Time Warping Distance.

Jiyuan An, Yi-Ping Phoebe Chen, Eamonn J. Keogh

Proceedings of the Advances in Web-Age Information Management: 5th International Conference, 2004

Indexing Large Human-Motion Databases.

Eamonn J. Keogh, Themis Palpanas, Victor B. Zordan, Dimitrios Gunopulos, Marc Cardle

Proceedings of the (e)Proceedings of the Thirtieth International Conference on Very Large Data Bases, VLDB 2004, Toronto, Canada, August 31, 2004

VizTree: a Tool for Visually Mining and Monitoring Massive Time Series Databases.

Jessica Lin, Eamonn J. Keogh, Stefano Lonardi, Jeffrey P. Lankford, Donna M. Nystrom

Proceedings of the (e)Proceedings of the Thirtieth International Conference on Very Large Data Bases, VLDB 2004, Toronto, Canada, August 31, 2004

Making Time-Series Classification More Accurate Using Learned Constraints.

Chotirat (Ann) Ratanamahatana, Eamonn J. Keogh

Proceedings of the Fourth SIAM International Conference on Data Mining, 2004

Visually mining and monitoring massive time series.

Jessica Lin, Eamonn J. Keogh, Stefano Lonardi, Jeffrey P. Lankford, Donna M. Nystrom

Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2004

Towards parameter-free data mining.

Eamonn J. Keogh, Stefano Lonardi, Chotirat (Ann) Ratanamahatana

Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2004

Online Amnesic Approximation of Streaming Time Series.

Themistoklis Palpanas, Michail Vlachos, Eamonn J. Keogh, Dimitrios Gunopulos, Wagner Truppel

Proceedings of the 20th International Conference on Data Engineering, 2004

Iterative Incremental Clustering of Time Series.

Jessica Lin, Michail Vlachos, Eamonn J. Keogh, Dimitrios Gunopulos

Proceedings of the Advances in Database Technology, 2004

We Have Seen the Future, and It Is Symbolic.

Eamonn J. Keogh, Jessica Lin, Stefano Lonardi, Bill Yuan-chi Chiu

Proceedings of the ACSW Frontiers 2004, 2004 ACSW Workshops, 2004

2003

On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration.

Eamonn J. Keogh, Shruti Kasetty

Data Min. Knowl. Discov., 2003

A Gentle Introduction to Machine Learning and Data Mining for the Database Community.

Eamonn J. Keogh

Proceedings of the XVIII Simpósio Brasileiro de Bancos de Dados, 2003

Efficiently Finding Arbitrarily Scaled Patterns in Massive Time Series Databases.

Eamonn J. Keogh

Proceedings of the Knowledge Discovery in Databases: PKDD 2003, 2003

Indexing multi-dimensional time-series with support for multiple distance measures.

Michail Vlachos, Marios Hadjieleftheriou, Dimitrios Gunopulos, Eamonn J. Keogh

Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 24, 2003

Probabilistic discovery of time series motifs.

Bill Yuan-chi Chiu, Eamonn J. Keogh, Stefano Lonardi

Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 24, 2003

Grid-Based Indexing for Large Time Series Databases.

Jiyuan An, Hanxiong Chen, Kazutaka Furuse, Nobuo Ohbo, Eamonn J. Keogh

Proceedings of the Intelligent Data Engineering and Automated Learning, 2003

Clustering of Time Series Subsequences is Meaningless: Implications for Previous and Future Research.

Eamonn J. Keogh, Jessica Lin, Wagner Truppel

Proceedings of the 3rd IEEE International Conference on Data Mining (ICDM 2003), 2003

(Not) Finding Rules in Time Series: A Surprising Result with Implications for Previous and Future Research.

Jessica Lin, Eamonn J. Keogh, Wagner Truppel

Proceedings of the International Conference on Artificial Intelligence, 2003

Clustering of streaming time series is meaningless.

Jessica Lin, Eamonn J. Keogh, Wagner Truppel

Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery, 2003

A symbolic representation of time series, with implications for streaming algorithms.

Jessica Lin, Eamonn J. Keogh, Stefano Lonardi, Bill Yuan-chi Chiu

Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery, 2003

2002

Locally adaptive dimensionality reduction for indexing large time series databases.

Kaushik Chakrabarti, Eamonn J. Keogh, Sharad Mehrotra, Michael J. Pazzani

ACM Trans. Database Syst., 2002

Learning the Structure of Augmented Bayesian Classifiers.

Eamonn J. Keogh, Michael J. Pazzani

Int. J. Artif. Intell. Tools, 2002

Exact Indexing of Dynamic Time Warping.

Eamonn J. Keogh

Proceedings of 28th International Conference on Very Large Data Bases, 2002

Iterative Deepening Dynamic Time Warping for Time Series.

Selina Chu, Eamonn J. Keogh, David M. Hart, Michael J. Pazzani

Proceedings of the Second SIAM International Conference on Data Mining, 2002

Indexing and Mining Time Series.

Eamonn J. Keogh

Proceedings of the XVII Simpósio Brasileiro de Banco de Dados, 2002

Finding surprising patterns in a time series database in linear time and space.

Eamonn J. Keogh, Stefano Lonardi, Bill Yuan-chi Chiu

Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2002

Mining Motifs in Massive Time Series Databases.

Pranav Patel, Eamonn J. Keogh, Jessica Lin, Stefano Lonardi

Proceedings of the 2002 IEEE International Conference on Data Mining (ICDM 2002), 2002

An Augmented Visual Query Mechanism for Finding Patterns in Time Series Data.

Eamonn J. Keogh, Harry Hochheiser, Ben Shneiderman

Proceedings of the Flexible Query Answering Systems, 5th International Conference, 2002

2001

Dimensionality Reduction for Fast Similarity Search in Large Time Series Databases.

Eamonn J. Keogh, Kaushik Chakrabarti, Michael J. Pazzani, Sharad Mehrotra

Knowl. Inf. Syst., 2001

Derivative Dynamic Time Warping.

Eamonn J. Keogh, Michael J. Pazzani

Proceedings of the First SIAM International Conference on Data Mining, 2001

Ensemble-index: a new approach to indexing large databases.

Eamonn J. Keogh, Selina Chu, Michael J. Pazzani

Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, 2001

An Online Algorithm for Segmenting Time Series.

Eamonn J. Keogh, Selina Chu, David M. Hart, Michael J. Pazzani

Proceedings of the 2001 IEEE International Conference on Data Mining, 29 November, 2001

2000

A Simple Dimensionality Reduction Technique for Fast Similarity Search in Large Time Series Databases.

Eamonn J. Keogh, Michael J. Pazzani

Proceedings of the Knowledge Discovery and Data Mining, 2000

Scaling up dynamic time warping for datamining applications.

Eamonn J. Keogh, Michael J. Pazzani

Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining, 2000

1999

Pseudo Periodic Synthetic Time Series.

Eamonn J. Keogh, Michael J. Pazzani

Dataset, February, 1999

An Indexing Scheme for Fast Similarity Search in Large Time Series Databases.

Eamonn J. Keogh, Michael J. Pazzani

Proceedings of the 11th International Conference on Scientific and Statistical Database Management, 1999

Relevance Feedback Retrieval of Time Series Data.

Eamonn J. Keogh, Michael J. Pazzani

Proceedings of the SIGIR '99: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 1999

Scaling up Dynamic Time Warping to Massive Dataset.

Eamonn J. Keogh, Michael J. Pazzani

Proceedings of the Principles of Data Mining and Knowledge Discovery, 1999

Learning augmented Bayesian classifiers: A comparison of distribution-based and classification-based approaches.

Eamonn J. Keogh, Michael J. Pazzani

Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics, 1999

1998

An Enhanced Representation of Time Series Which Allows Fast and Accurate Classification, Clustering and Relevance Feedback.

Eamonn J. Keogh, Michael J. Pazzani

Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining (KDD-98), 1998

1997

A Probabilistic Approach to Fast Pattern Matching in Time Series Databases.

Eamonn J. Keogh, Padhraic Smyth

Proceedings of the Third International Conference on Knowledge Discovery and Data Mining (KDD-97), 1997

Fast Similarity Search in the Presence of Longitudinal Scaling in Time Series Databases.

Eamonn J. Keogh

Proceedings of the 9th International Conference on Tools with Artificial Intelligence, 1997