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ORCIDEamonn J. Keogh
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- affiliation: University of California, Riverside, USA
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2020 – today
- 2024
[j79]Maryam Shahcheraghi, Ryan Mercer, João Manuel De Almeida Rodrigues, Audrey Der, Hugo Filipe Silveira Gamboa, Zachary Zimmerman, Kerry Mauck, Eamonn J. Keogh:
Introducing Mplots: scaling time series recurrence plots to massive datasets. J. Big Data 11(1): 96 (2024)- [j78]Ryan Mercer, Eamonn J. Keogh:
Novelets: a new primitive that allows online detection of emerging behaviors in time series. Knowl. Inf. Syst. 66(1): 59-87 (2024) - [j77]Sadaf Tafazoli, Yue Lu, Renjie Wu
, Thirumalai Vinjamoor Akhil Srinivas, Hannah Dela Cruz, Ryan Mercer, Eamonn J. Keogh:
C22MP: the marriage of catch22 and the matrix profile creates a fast, efficient and interpretable anomaly detector. Knowl. Inf. Syst. 66(8): 4789-4823 (2024) - [c201]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:
A Systematic Evaluation of Generated Time Series and Their Effects in Self-Supervised Pretraining. CIKM 2024: 3719-3723 - [c200]Eamonn J. Keogh:
Time Series Data Mining: A Unifying View. DSAA 2024: 1-3 - [c199]Audrey Der, Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Zhongfang Zhuang, Liang Wang, Wei Zhang, Eamonn J. Keogh:
PUPAE: Intuitive and Actionable Explanations for Time Series Anomalies. SDM 2024: 37-45 - [i25]Audrey Der, Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Zhongfang Zhuang, Liang Wang, Wei Zhang, Eamonn J. Keogh:
PUPAE: Intuitive and Actionable Explanations for Time Series Anomalies. CoRR abs/2401.09489 (2024) - [i24]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:
A Systematic Evaluation of Generated Time Series and Their Effects in Self-Supervised Pretraining. CoRR abs/2408.07869 (2024) - [i23]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:
Matrix Profile for Anomaly Detection on Multidimensional Time Series. CoRR abs/2409.09298 (2024) - 2023
- [j76]Yue Lu, Renjie Wu, Abdullah Mueen, Maria A. Zuluaga, Eamonn J. Keogh:
DAMP: accurate time series anomaly detection on trillions of datapoints and ultra-fast arriving data streams. Data Min. Knowl. Discov. 37(2): 627-669 (2023) - [j75]Takaaki Nakamura, Ryan Mercer, Makoto Imamura, Eamonn J. Keogh:
MERLIN++: parameter-free discovery of time series anomalies. Data Min. Knowl. Discov. 37(2): 670-709 (2023) - [j74]Eamonn J. Keogh:
Time Series Data Mining: A Unifying View. Proc. VLDB Endow. 16(12): 3861-3863 (2023) - [j73]Renjie Wu, Eamonn J. Keogh:
Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of Progress. IEEE Trans. Knowl. Data Eng. 35(3): 2421-2429 (2023) - [j72]Renjie Wu, Audrey Der, Eamonn J. Keogh:
When is Early Classification of Time Series Meaningful? IEEE Trans. Knowl. Data Eng. 35(3): 3253-3260 (2023) - [c198]Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M. Phillips, Eamonn J. Keogh:
Sketching Multidimensional Time Series for Fast Discord Mining. IEEE Big Data 2023: 443-452 - [c197]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:
Ego-Network Transformer for Subsequence Classification in Time Series Data. IEEE Big Data 2023: 1242-1247 - [c196]Audrey Der, Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Liang Wang, Wei Zhang, Eamonn J. Keogh:
Time Series Synthesis Using the Matrix Profile for Anonymization. IEEE Big Data 2023: 1908-1911 - [c195]Prithviraj Yuvaraj, Amin Akalantar, Eamonn J. Keogh, Philip Brisk:
Feature Extraction Accelerator for Streaming Time Series. FCCM 2023: 207 - [c194]Sadaf Tafazoli, Yue Lu, Renjie Wu, Thirumalai Vinjamoor Akhil Srinivas, Hannah Dela Cruz, Ryan Mercer, Eamonn J. Keogh:
Matrix Profile XXIX: C22MP, Fusing catch 22 and the Matrix Profile to Produce an Efficient and Interpretable Anomaly Detector. ICDM 2023: 568-577 - [c193]Yue Lu, Thirumalai Vinjamoor Akhil Srinivas, Takaaki Nakamura, Makoto Imamura, Eamonn J. Keogh:
Matrix Profile XXX: MADRID: A Hyper-Anytime and Parameter-Free Algorithm to Find Time Series Anomalies of all Lengths. ICDM 2023: 1199-1204 - [c192]Seyhan Ucar, Ryan Mercer, Eamonn J. Keogh:
Tailgating Behavior Detection On Rear Vehicles. ITSC 2023: 440-445 - [c191]Eamonn J. Keogh:
Getting an h-Index of 100 in 20 Years or Less! KDD 2023: 5807-5808 - [c190]Sadaf Tafazoli, Eamonn J. Keogh:
Matrix Profile XXVIII: Discovering Multi-Dimensional Time Series Anomalies with K of N Anomaly Detection†. SDM 2023: 685-693 - [i22]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:
Ego-Network Transformer for Subsequence Classification in Time Series Data. CoRR abs/2311.02561 (2023) - [i21]Audrey Der, Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Liang Wang, Wei Zhang, Eamonn J. Keogh:
Time Series Synthesis Using the Matrix Profile for Anonymization. CoRR abs/2311.02563 (2023) - [i20]Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan, Huiyuan Chen, Zhongfang Zhuang, Junpeng Wang, Liang Wang, Wei Zhang, Jeff M. Phillips, Eamonn J. Keogh:
Sketching Multidimensional Time Series for Fast Discord Mining. CoRR abs/2311.03393 (2023) - 2022
- [j71]Ryan Mercer, Sara Alaee, Alireza Abdoli, Nader Shakibay Senobari, Shailendra Singh, Amy C. Murillo, Eamonn J. Keogh:
Introducing the contrast profile: a novel time series primitive that allows real world classification. Data Min. Knowl. Discov. 36(2): 877-915 (2022) - [j70]Renjie Wu, Eamonn J. Keogh:
FastDTW is Approximate and Generally Slower Than the Algorithm it Approximates. IEEE Trans. Knowl. Data Eng. 34(8): 3779-3785 (2022) - [c189]Audrey Der, Chin-Chia Michael Yeh, Renjie Wu, Junpeng Wang, Yan Zheng, Zhongfang Zhuang, Liang Wang, Wei Zhang, Eamonn J. Keogh:
Matrix Profile XXVII: A Novel Distance Measure for Comparing Long Time Series. ICKG 2022: 40-47 - [c188]Renjie Wu, Audrey Der, Eamonn J. Keogh:
When is Early Classification of Time Series Meaningful? (Extended Abstract). ICDE 2022: 1477-1478 - [c187]Renjie Wu, Eamonn J. Keogh:
Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of Progress (Extended Abstract). ICDE 2022: 1479-1480 - [c186]Ryan Mercer, Eamonn J. Keogh:
Matrix Profile XXV: Introducing Novelets: A Primitive that Allows Online Detection of Emerging Behaviors in Time Series. ICDM 2022: 338-347 - [c185]Maryam Shahcheraghi, Ryan Mercer, João Manuel De Almeida Rodrigues, Audrey Der, Hugo Filipe Silveira Gamboa, Zachary Zimmerman, Eamonn J. Keogh:
Matrix Profile XXVI: Mplots: Scaling Time Series Similarity Matrices to Massive Data. ICDM 2022: 1179-1184 - [c184]Yue Lu, Renjie Wu, Abdullah Mueen, Maria A. Zuluaga, Eamonn J. Keogh:
Matrix Profile XXIV: Scaling Time Series Anomaly Detection to Trillions of Datapoints and Ultra-fast Arriving Data Streams. KDD 2022: 1173-1182 - [c183]Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, Eamonn J. Keogh:
Error-bounded Approximate Time Series Joins using Compact Dictionary Representations of Time Series. SDM 2022: 181-189 - [i19]Audrey Der, Chin-Chia Michael Yeh, Renjie Wu, Junpeng Wang, Yan Zheng, Zhongfang Zhuang, Liang Wang, Wei Zhang, Eamonn J. Keogh:
Matrix Profile XXVII: A Novel Distance Measure for Comparing Long Time Series. CoRR abs/2212.06146 (2022) - 2021
- [j69]Sara Alaee, Ryan Mercer, Kaveh Kamgar, Eamonn J. Keogh:
Time series motifs discovery under DTW allows more robust discovery of conserved structure. Data Min. Knowl. Discov. 35(3): 863-910 (2021) - [j68]Yan Zhu, Abdullah Mueen, Eamonn J. Keogh:
Matrix Profile IX: Admissible Time Series Motif Discovery With Missing Data. IEEE Trans. Knowl. Data Eng. 33(6): 2616-2626 (2021) - [c182]Maryam Shahcheraghi, Trevor Cappon, Samet Oymak, Evangelos E. Papalexakis, Eamonn J. Keogh, Zachary Zimmerman, Philip Brisk:
Matrix Profile Index Approximation for Streaming Time Series. IEEE BigData 2021: 2775-2784 - [c181]Ryan Mercer, Seyhan Ucar, Eamonn J. Keogh:
Shape-Based Telemetry Approach for Distracted Driving Behavior Detection. CSCN 2021: 118-123 - [c180]Renjie Wu, Eamonn J. Keogh:
FastDTW is approximate and Generally Slower than the Algorithm it Approximates (Extended Abstract). ICDE 2021: 2327-2328 - [c179]Ryan Mercer, Sara Alaee, Alireza Abdoli, Shailendra Singh, Amy C. Murillo, Eamonn J. Keogh:
Matrix Profile XXIII: Contrast Profile: A Novel Time Series Primitive that Allows Real World Classification. ICDM 2021: 1240-1245 - [i18]Renjie Wu, Audrey Der, Eamonn J. Keogh:
When is Early Classification of Time Series Meaningful? CoRR abs/2102.11487 (2021) - [i17]Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Wei Zhang, Eamonn J. Keogh:
Error-bounded Approximate Time Series Joins using Compact Dictionary Representations of Time Series. CoRR abs/2112.12965 (2021) - 2020
- [j67]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:
The Swiss army knife of time series data mining: ten useful things you can do with the matrix profile and ten lines of code. Data Min. Knowl. Discov. 34(4): 949-979 (2020) - [j66]Michele Linardi, Yan Zhu, Themis Palpanas, Eamonn J. Keogh:
Matrix profile goes MAD: variable-length motif and discord discovery in data series. Data Min. Knowl. Discov. 34(4): 1022-1071 (2020) - [j65]Shaghayegh Gharghabi, Shima Imani, Anthony J. Bagnall, Amirali Darvishzadeh, Eamonn J. Keogh:
An ultra-fast time series distance measure to allow data mining in more complex real-world deployments. Data Min. Knowl. Discov. 34(4): 1104-1135 (2020) - [j64]Shima Imani, Frank Madrid, Wei Ding, Scott E. Crouter, Eamonn J. Keogh:
Introducing time series snippets: a new primitive for summarizing long time series. Data Min. Knowl. Discov. 34(6): 1713-1743 (2020) - [c178]Yan Zhu, Chin-Chia Michael Yeh, Zachary Zimmerman, Eamonn J. Keogh:
Matrix Profile XVII: Indexing the Matrix Profile to Allow Arbitrary Range Queries. ICDE 2020: 1846-1849 - [c177]Sara Alaee, Kaveh Kamgar, Eamonn J. Keogh:
Matrix Profile XXII: Exact Discovery of Time Series Motifs under DTW. ICDM 2020: 900-905 - [c176]Takaaki Nakamura, Makoto Imamura, Ryan Mercer, Eamonn J. Keogh:
MERLIN: Parameter-Free Discovery of Arbitrary Length Anomalies in Massive Time Series Archives. ICDM 2020: 1190-1195 - [c175]Makoto Imamura, Takaaki Nakamura, Eamonn J. Keogh:
Matrix Profile XXI: A Geometric Approach to Time Series Chains Improves Robustness. KDD 2020: 1114-1122 - [c174]Alireza Abdoli, Sara Alaee, Shima Imani, Amy C. Murillo, Alec C. Gerry, Leslie Hickle, Eamonn J. Keogh:
Fitbit for Chickens?: Time Series Data Mining Can Increase the Productivity of Poultry Farms. KDD 2020: 3328-3336 - [c173]Sara Alaee, Alireza Abdoli, Christian R. Shelton, Amy C. Murillo, Alec C. Gerry, Eamonn J. Keogh:
Features or Shape? Tackling the False Dichotomy of Time Series Classification. SDM 2020: 442-450 - [c172]Shima Imani, Eamonn J. Keogh:
Natura: Towards Conversational Analytics for Comparing and Contrasting Time Series. WWW (Companion Volume) 2020: 46-47 - [i16]Renjie Wu, Eamonn J. Keogh:
FastDTW is approximate and Generally Slower than the Algorithm it Approximates. CoRR abs/2003.11246 (2020) - [i15]Michele Linardi, Yan Zhu, Themis Palpanas, Eamonn J. Keogh:
VALMOD: A Suite for Easy and Exact Detection of Variable Length Motifs in Data Series. CoRR abs/2008.13432 (2020) - [i14]Michele Linardi, Yan Zhu, Themis Palpanas, Eamonn J. Keogh:
Matrix Profile Goes MAD: Variable-Length Motif And Discord Discovery in Data Series. CoRR abs/2008.13447 (2020) - [i13]Sara Alaee, Kaveh Kamgar, Eamonn J. Keogh:
Matrix Profile XXII: Exact Discovery of Time Series Motifs under DTW. CoRR abs/2009.07907 (2020) - [i12]Renjie Wu, Eamonn J. Keogh:
Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of Progress. CoRR abs/2009.13807 (2020)
2010 – 2019
- 2019
- [j63]Shaghayegh Gharghabi, Chin-Chia Michael Yeh, Yifei Ding, Wei Ding, Paul Hibbing, Samuel LaMunion, Andrew Kaplan, Scott E. Crouter, Eamonn J. Keogh:
Domain agnostic online semantic segmentation for multi-dimensional time series. Data Min. Knowl. Discov. 33(1): 96-130 (2019) - [j62]Shaghayegh Gharghabi, Chin-Chia Michael Yeh, Yifei Ding, Wei Ding, Paul Hibbing, Samuel LaMunion, Andrew Kaplan, Scott E. Crouter, Eamonn J. Keogh:
Correction to: Domain agnostic online semantic segmentation for multi-dimensional time series. Data Min. Knowl. Discov. 33(6): 1981-1982 (2019) - [j61]Hoang Anh Dau, Anthony J. Bagnall, Kaveh Kamgar, Chin-Chia Michael Yeh, Yan Zhu, Shaghayegh Gharghabi, Chotirat Ann Ratanamahatana, Eamonn J. Keogh:
The UCR time series archive. IEEE CAA J. Autom. Sinica 6(6): 1293-1305 (2019) - [j60]Yan Zhu, Makoto Imamura, Daniel Nikovski, Eamonn J. Keogh:
Introducing time series chains: a new primitive for time series data mining. Knowl. Inf. Syst. 60(2): 1135-1161 (2019) - [j59]Diego Furtado Silva, Chin-Chia Michael Yeh, Yan Zhu, Gustavo E. A. P. A. Batista, Eamonn J. Keogh:
Fast Similarity Matrix Profile for Music Analysis and Exploration. IEEE Trans. Multim. 21(1): 29-38 (2019) - [c171]Alireza Abdoli, Amy C. Murillo, Alec C. Gerry, Eamonn J. Keogh:
Time Series Classification: Lessons Learned in the (Literal) Field while Studying Chicken Behavior. IEEE BigData 2019: 5962-5964 - [c170]Zachary Zimmerman, Kaveh Kamgar, Nader Shakibay Senobari, Brian Crites, Gareth J. Funning, Philip Brisk, Eamonn J. Keogh:
Matrix Profile XIV: Scaling Time Series Motif Discovery with GPUs to Break a Quintillion Pairwise Comparisons a Day and Beyond. SoCC 2019: 74-86 - [c169]Frank Madrid, Shailendra Singh, Quentin Chesnais, Kerry Mauck, Eamonn J. Keogh:
Matrix Profile XVI: Efficient and Effective Labeling of Massive Time Series Archives. DSAA 2019: 463-472 - [c168]Frank Madrid, Shima Imani, Ryan Mercer, Zachary Zimmerman, Nader Shakibay Senobari, Eamonn J. Keogh:
Matrix Profile XX: Finding and Visualizing Time Series Motifs of All Lengths using the Matrix Profile. ICBK 2019: 175-182 - [c167]Shima Imani, Eamonn J. Keogh:
Matrix Profile XIX: Time Series Semantic Motifs: A New Primitive for Finding Higher-Level Structure in Time Series. ICDM 2019: 329-338 - [c166]Zachary Zimmerman, Nader Shakibay Senobari, Gareth J. Funning, Evangelos E. Papalexakis, Samet Oymak, Philip Brisk, Eamonn J. Keogh:
Matrix Profile XVIII: Time Series Mining in the Face of Fast Moving Streams using a Learned Approximate Matrix Profile. ICDM 2019: 936-945 - [c165]Kaveh Kamgar, Shaghayegh Gharghabi, Eamonn J. Keogh:
Matrix Profile XV: Exploiting Time Series Consensus Motifs to Find Structure in Time Series Sets. ICDM 2019: 1156-1161 - [c164]Chin-Chia Michael Yeh, Yan Zhu, Hoang Anh Dau, Amirali Darvishzadeh, Mikhail Noskov, Eamonn J. Keogh:
Online Amnestic DTW to allow Real-Time Golden Batch Monitoring. KDD 2019: 2604-2612 - [c163]Shima Imani, Sara Alaee, Eamonn J. Keogh:
Putting the Human in the Time Series Analytics Loop. WWW (Companion Volume) 2019: 635-644 - [i11]Chang Wei Tan, François Petitjean, Eamonn J. Keogh, Geoffrey I. Webb:
Time series classification for varying length series. CoRR abs/1910.04341 (2019) - [i10]Alireza Abdoli, Amy C. Murillo, Alec C. Gerry, Eamonn J. Keogh:
Time Series Classification: Lessons Learned in the (Literal) Field while Studying Chicken Behavior. CoRR abs/1912.05913 (2019) - [i9]Sara Alaee, Alireza Abdoli, Christian R. Shelton, Amy C. Murillo, Alec C. Gerry, Eamonn J. Keogh:
Features or Shape? Tackling the False Dichotomy of Time Series Classification. CoRR abs/1912.09614 (2019) - 2018
- [j58]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:
Time series joins, motifs, discords and shapelets: a unifying view that exploits the matrix profile. Data Min. Knowl. Discov. 32(1): 83-123 (2018) - [j57]Diego Furtado Silva, Rafael Giusti, Eamonn J. Keogh, Gustavo E. A. P. A. Batista:
Speeding up similarity search under dynamic time warping by pruning unpromising alignments. Data Min. Knowl. Discov. 32(4): 988-1016 (2018) - [j56]Hoang Anh Dau, Diego Furtado Silva, François Petitjean, Germain Forestier, Anthony J. Bagnall, Abdullah Mueen, Eamonn J. Keogh:
Optimizing dynamic time warping's window width for time series data mining applications. Data Min. Knowl. Discov. 32(4): 1074-1120 (2018) - [j55]Yan Zhu, Zachary Zimmerman, Nader Shakibay Senobari, Chin-Chia Michael Yeh, Gareth J. Funning, Abdullah Mueen, Philip Brisk, Eamonn J. Keogh:
Exploiting a novel algorithm and GPUs to break the ten quadrillion pairwise comparisons barrier for time series motifs and joins. Knowl. Inf. Syst. 54(1): 203-236 (2018) - [c162]Shima Imani, Frank Madrid, Wei Ding, Scott E. Crouter, Eamonn J. Keogh:
Matrix Profile XIII: Time Series Snippets: A New Primitive for Time Series Data Mining. ICBK 2018: 382-389 - [c161]Rodica Neamtu, Ramoza Ahsan, Elke A. Rundensteiner, Gábor N. Sárközy, Eamonn J. Keogh, Hoang Anh Dau, Cuong Nguyen, Charles Lovering:
Generalized Dynamic Time Warping: Unleashing the Warping Power Hidden in Point-Wise Distances. ICDE 2018: 521-532 - [c160]Yan Zhu, Chin-Chia Michael Yeh, Zachary Zimmerman, Kaveh Kamgar, Eamonn J. Keogh:
Matrix Profile XI: SCRIMP++: Time Series Motif Discovery at Interactive Speeds. ICDM 2018: 837-846 - [c159]Shaghayegh Gharghabi, Shima Imani, Anthony J. Bagnall, Amirali Darvishzadeh, Eamonn J. Keogh:
Matrix Profile XII: MPdist: A Novel Time Series Distance Measure to Allow Data Mining in More Challenging Scenarios. ICDM 2018: 965-970 - [c158]Alireza Abdoli, Amy C. Murillo, Chin-Chia Michael Yeh, Alec C. Gerry, Eamonn J. Keogh:
Time Series Classification to Improve Poultry Welfare. ICMLA 2018: 635-642 - [c157]Yan Zhu, Makoto Imamura, Daniel Nikovski, Eamonn J. Keogh:
Time Series Chains: A Novel Tool for Time Series Data Mining. IJCAI 2018: 5414-5418 - [c156]Yilin Shen, Yanping Chen, Eamonn J. Keogh, Hongxia Jin:
Accelerating Time Series Searching with Large Uniform Scaling. SDM 2018: 234-242 - [c155]Michele Linardi, Yan Zhu, Themis Palpanas, Eamonn J. Keogh:
Matrix Profile X: VALMOD - Scalable Discovery of Variable-Length Motifs in Data Series. SIGMOD Conference 2018: 1053-1066 - [c154]Michele Linardi, Yan Zhu, Themis Palpanas, Eamonn J. Keogh:
VALMOD: A Suite for Easy and Exact Detection of Variable Length Motifs in Data Series. SIGMOD Conference 2018: 1757-1760 - [i8]Yan Zhu, Abdullah Mueen, Eamonn J. Keogh:
Admissible Time Series Motif Discovery with Missing Data. CoRR abs/1802.05472 (2018) - [i7]Hoang Anh Dau, Anthony J. Bagnall, Kaveh Kamgar, Chin-Chia Michael Yeh, Yan Zhu, Shaghayegh Gharghabi, Chotirat Ann Ratanamahatana, Eamonn J. Keogh:
The UCR Time Series Archive. CoRR abs/1810.07758 (2018) - [i6]Anthony J. Bagnall, Hoang Anh Dau, Jason Lines, Michael Flynn, James Large, Aaron Bostrom, Paul Southam, Eamonn J. Keogh:
The UEA multivariate time series classification archive, 2018. CoRR abs/1811.00075 (2018) - [i5]Chin-Chia Michael Yeh, Yan Zhu, Evangelos E. Papalexakis, Abdullah Mueen, Eamonn J. Keogh:
Representation Learning by Reconstructing Neighborhoods. CoRR abs/1811.01557 (2018) - [i4]Alireza Abdoli, Amy C. Murillo, Chin-Chia Michael Yeh, Alec C. Gerry, Eamonn J. Keogh:
Time Series Classification to Improve Poultry Welfare. CoRR abs/1811.03149 (2018) - 2017
- [j54]Mohammad Shokoohi-Yekta, Bing Hu, Hongxia Jin, Jun Wang, Eamonn J. Keogh:
Generalizing DTW to the multi-dimensional case requires an adaptive approach. Data Min. Knowl. Discov. 31(1): 1-31 (2017) - [j53]Usue Mori, Alexander Mendiburu, Eamonn J. Keogh, José Antonio Lozano:
Reliable early classification of time series based on discriminating the classes over time. Data Min. Knowl. Discov. 31(1): 233-263 (2017) - [j52]Anthony J. Bagnall, Jason Lines, Aaron Bostrom, James Large, Eamonn J. Keogh:
The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances. Data Min. Knowl. Discov. 31(3): 606-660 (2017) - [j51]Chin-Chia Michael Yeh, Nickolas Kavantzas, Eamonn J. Keogh:
Matrix Profile IV: Using Weakly Labeled Time Series to Predict Outcomes. Proc. VLDB Endow. 10(12): 1802-1812 (2017) - [c153]Hoang Anh Dau, Diego Furtado Silva, François Petitjean, Germain Forestier, Anthony J. Bagnall, Eamonn J. Keogh:
Judicious setting of Dynamic Time Warping's window width allows more accurate classification of time series. IEEE BigData 2017: 917-922 - [c152]Yilin Shen, Yanping Chen, Eamonn J. Keogh, Hongxia Jin:
Searching Time Series with Invariance to Large Amounts of Uniform Scaling. ICDE 2017: 111-114 - [c151]Shaghayegh Gharghabi, Yifei Ding, Chin-Chia Michael Yeh, Kaveh Kamgar, Liudmila Ulanova, Eamonn J. Keogh:
Matrix Profile VIII: Domain Agnostic Online Semantic Segmentation at Superhuman Performance Levels. ICDM 2017: 117-126 - [c150]Chin-Chia Michael Yeh, Nickolas Kavantzas, Eamonn J. Keogh:
Matrix Profile VI: Meaningful Multidimensional Motif Discovery. ICDM 2017: 565-574 - [c149]Yan Zhu, Makoto Imamura, Daniel Nikovski, Eamonn J. Keogh:
Matrix Profile VII: Time Series Chains: A New Primitive for Time Series Data Mining (Best Student Paper Award). ICDM 2017: 695-704 - [c148]Germain Forestier, François Petitjean, Hoang Anh Dau, Geoffrey I. Webb, Eamonn J. Keogh:
Generating Synthetic Time Series to Augment Sparse Datasets. ICDM 2017: 865-870 - [c147]Hoang Anh Dau, Eamonn J. Keogh:
Matrix Profile V: A Generic Technique to Incorporate Domain Knowledge into Motif Discovery. KDD 2017: 125-134 - [c146]Yifei Ding, Eamonn J. Keogh:
Query Suggestion to allow Intuitive Interactive Search in Multidimensional Time Series. SSDBM 2017: 18:1-18:11 - [r12]Eamonn J. Keogh:
Indexing and Mining Time Series Data. Encyclopedia of GIS 2017: 933-939 - [r11]Eamonn J. Keogh, Abdullah Mueen:
Curse of Dimensionality. Encyclopedia of Machine Learning and Data Mining 2017: 314-315 - [r10]Eamonn J. Keogh:
Instance-Based Learning. Encyclopedia of Machine Learning and Data Mining 2017: 672-673 - [r9]Eamonn J. Keogh:
Nearest Neighbor. Encyclopedia of Machine Learning and Data Mining 2017: 897 - [r8]Eamonn J. Keogh:
Time Series. Encyclopedia of Machine Learning and Data Mining 2017: 1274-1275 - 2016
- [j50]Jesin Zakaria, Abdullah Mueen, Eamonn J. Keogh, Neal E. Young:
Accelerating the discovery of unsupervised-shapelets. Data Min. Knowl. Discov. 30(1): 243-281 (2016) - [j49]Bing Hu, Yanping Chen, Eamonn J. Keogh:
Classification of streaming time series under more realistic assumptions. Data Min. Knowl. Discov. 30(2): 403-437 (2016) - [j48]Yan Zhu, Eamonn J. Keogh:
Irrevocable-choice algorithms for sampling from a stream. Data Min. Knowl. Discov. 30(5): 998-1023 (2016) - [j47]François Petitjean, Germain Forestier, Geoffrey I. Webb, Ann E. Nicholson, Yanping Chen, Eamonn J. Keogh:
Faster and more accurate classification of time series by exploiting a novel dynamic time warping averaging algorithm. Knowl. Inf. Syst. 47(1): 1-26 (2016) - [c145]Hoang Anh Dau, Nurjahan Begum, Eamonn J. Keogh:
Semi-Supervision Dramatically Improves Time Series Clustering under Dynamic Time Warping. CIKM 2016: 999-1008 - [c144]Chin-Chia Michael Yeh, Helga Van Herle, Eamonn J. Keogh:
Matrix Profile III: The Matrix Profile Allows Visualization of Salient Subsequences in Massive Time Series. ICDM 2016: 579-588 - [c143]Yan Zhu, Zachary Zimmerman, Nader Shakibay Senobari, Chin-Chia Michael Yeh, Gareth J. Funning, Abdullah Mueen, Philip Brisk, Eamonn J. Keogh:
Matrix Profile II: Exploiting a Novel Algorithm and GPUs to Break the One Hundred Million Barrier for Time Series Motifs and Joins. ICDM 2016: 739-748 - [c142]Diego Furtado Silva, Gustavo E. A. P. A. Batista, Eamonn J. Keogh:
Prefix and Suffix Invariant Dynamic Time Warping. ICDM 2016: 1209-1214 - [c141]Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, Eamonn J. Keogh:
Matrix Profile I: All Pairs Similarity Joins for Time Series: A Unifying View That Includes Motifs, Discords and Shapelets. ICDM 2016: 1317-1322 - [c140]Diego Furtado Silva, Chin-Chia Michael Yeh, Gustavo E. A. P. A. Batista, Eamonn J. Keogh:
SiMPle: Assessing Music Similarity Using Subsequences Joins. ISMIR 2016: 23-29 - [c139]Abdullah Mueen, Eamonn J. Keogh:
Extracting Optimal Performance from Dynamic Time Warping. KDD 2016: 2129-2130 - [c138]Liudmila Ulanova, Nurjahan Begum, Mohammad Shokoohi-Yekta, Eamonn J. Keogh:
Clustering in the Face of Fast Changing Streams. SDM 2016: 1-9 - [i3]Nurjahan Begum, Liudmila Ulanova, Hoang Anh Dau, Jun Wang, Eamonn J. Keogh:
A General Framework for Density Based Time Series Clustering Exploiting a Novel Admissible Pruning Strategy. CoRR abs/1612.00637 (2016) - 2015
- [j46]Bing Hu, Thanawin Rakthanmanon, Yuan Hao, Scott Evans, Stefano Lonardi, Eamonn J. Keogh:
Using the minimum description length to discover the intrinsic cardinality and dimensionality of time series. Data Min. Knowl. Discov. 29(2): 358-399 (2015) - [j45]Yanping Chen, Yuan Hao, Thanawin Rakthanmanon, Jesin Zakaria, Bing Hu, Eamonn J. Keogh:
A general framework for never-ending learning from time series streams. Data Min. Knowl. Discov. 29(6): 1622-1664 (2015) - [j44]Diego Furtado Silva, Vinícius M. A. de Souza, Daniel P. W. Ellis, Eamonn J. Keogh, Gustavo E. A. P. A. Batista:
Exploring Low Cost Laser Sensors to Identify Flying Insect Species - Evaluation of Machine Learning and Signal Processing Methods. J. Intell. Robotic Syst. 80(Supplement-1): 313-330 (2015) - [j43]Bing Hu, Thanawin Rakthanmanon, Bilson J. L. Campana, Abdullah Mueen, Eamonn J. Keogh:
Establishing the provenance of historical manuscripts with a novel distance measure. Pattern Anal. Appl. 18(2): 313-331 (2015) - [c137]Nurjahan Begum, Liudmila Ulanova, Jun Wang, Eamonn J. Keogh:
Accelerating Dynamic Time Warping Clustering with a Novel Admissible Pruning Strategy. KDD 2015: 49-58 - [c136]Mohammad Shokoohi-Yekta, Yanping Chen, Bilson J. L. Campana, Bing Hu, Jesin Zakaria, Eamonn J. Keogh:
Discovery of Meaningful Rules in Time Series. KDD 2015: 1085-1094 - [c135]Liudmila Ulanova, Tan Yan, Haifeng Chen, Guofei Jiang, Eamonn J. Keogh, Kai Zhang:
Efficient Long-Term Degradation Profiling in Time Series for Complex Physical Systems. KDD 2015: 2167-2176 - [c134]Mohammad Shokoohi-Yekta, Jun Wang, Eamonn J. Keogh:
On the Non-Trivial Generalization of Dynamic Time Warping to the Multi-Dimensional Case. SDM 2015: 289-297 - [c133]Liudmila Ulanova, Nurjahan Begum, Eamonn J. Keogh:
Scalable Clustering of Time Series with U-Shapelets. SDM 2015: 900-908 - 2014
- [j42]Gustavo E. A. P. A. Batista, Eamonn J. Keogh, Oben Moses Tataw, Vinícius M. A. de Souza:
CID: an efficient complexity-invariant distance for time series. Data Min. Knowl. Discov. 28(3): 634-669 (2014) - [j41]Alessandro Camerra, Jin Shieh, Themis Palpanas, Thanawin Rakthanmanon, Eamonn J. Keogh:
Beyond one billion time series: indexing and mining very large time series collections with i SAX2+. Knowl. Inf. Syst. 39(1): 123-151 (2014) - [j40]Nurjahan Begum, Eamonn J. Keogh:
Rare Time Series Motif Discovery from Unbounded Streams. Proc. VLDB Endow. 8(2): 149-160 (2014) - [c132]Joseph Tarango, Eamonn J. Keogh, Philip Brisk:
Accelerating the dynamic time warping distance measure using logarithmetic arithmetic. ACSSC 2014: 404-408 - [c131]François Petitjean, Germain Forestier, Geoffrey I. Webb, Ann E. Nicholson, Yanping Chen, Eamonn J. Keogh:
Dynamic Time Warping Averaging of Time Series Allows Faster and More Accurate Classification. ICDM 2014: 470-479 - [c130]Liudmila Ulanova, Yuan Hao, Eamonn J. Keogh:
Generating Synthetic Data to Allow Learning from a Single Exemplar per Class. SISAP 2014: 189-194 - [i2]Yanping Chen, Adena Why, Gustavo E. A. P. A. Batista, Agenor Mafra-Neto, Eamonn J. Keogh:
Flying Insect Classification with Inexpensive Sensors. CoRR abs/1403.2654 (2014) - 2013
- [j39]Xiaoyue Wang, Abdullah Mueen, Hui Ding, Goce Trajcevski, Peter Scheuermann, Eamonn J. Keogh:
Experimental comparison of representation methods and distance measures for time series data. Data Min. Knowl. Discov. 26(2): 275-309 (2013) - [j38]Oben M. Tataw, G. Venugopala Reddy, Eamonn J. Keogh, Amit K. Roy-Chowdhury:
Quantitative Analysis of Live-Cell Growth at the Shoot Apex of Arabidopsis thaliana: Algorithms for Feature Measurement and Temporal Alignment. IEEE ACM Trans. Comput. Biol. Bioinform. 10(5): 1150-1161 (2013) - [j37]Thanawin Rakthanmanon, Bilson J. L. Campana, Abdullah Mueen, Gustavo E. A. P. A. Batista, M. Brandon Westover, Qiang Zhu, Jesin Zakaria, Eamonn J. Keogh:
Addressing Big Data Time Series: Mining Trillions of Time Series Subsequences Under Dynamic Time Warping. ACM Trans. Knowl. Discov. Data 7(3): 10:1-10:31 (2013) - [c129]Joseph Tarango, Eamonn J. Keogh, Philip Brisk:
Instruction set extensions for Dynamic Time Warping. CODES+ISSS 2013: 18:1-18:10 - [c128]Oben M. Tataw, Thanawin Rakthanmanon, Eamonn J. Keogh:
Clustering of Symbols Using Minimal Description Length. ICDAR 2013: 180-184 - [c127]Yuan Hao, Mohammad Shokoohi-Yekta, George Papageorgiou, Eamonn J. Keogh:
Parameter-Free Audio Motif Discovery in Large Data Archives. ICDM 2013: 261-270 - [c126]Bing Hu, Yanping Chen, Jesin Zakaria, Liudmila Ulanova, Eamonn J. Keogh:
Classification of Multi-dimensional Streaming Time Series by Weighting Each Classifier's Track Record. ICDM 2013: 281-290 - [c125]Diego Furtado Silva, Vinícius M. A. de Souza, Gustavo E. A. P. A. Batista, Eamonn J. Keogh, Daniel P. W. Ellis:
Applying Machine Learning and Audio Analysis Techniques to Insect Recognition in Intelligent Traps. ICMLA (1) 2013: 99-104 - [c124]Thanawin Rakthanmanon, Eamonn J. Keogh:
Data Mining a Trillion Time Series Subsequences Under Dynamic Time Warping. IJCAI 2013: 3047-3051 - [c123]Nurjahan Begum, Bing Hu, Thanawin Rakthanmanon, Eamonn J. Keogh:
A Minimum Description Length Technique for Semi-Supervised Time Series Classification. IRI (best papers) 2013: 171-192 - [c122]Nurjahan Begum, Bing Hu, Thanawin Rakthanmanon, Eamonn J. Keogh:
Towards a minimum description length based stopping criterion for semi-supervised time series classification. IRI 2013: 333-340 - [c121]Yanping Chen, Bing Hu, Eamonn J. Keogh, Gustavo E. A. P. A. Batista:
DTW-D: time series semi-supervised learning from a single example. KDD 2013: 383-391 - [c120]Yuan Hao, Yanping Chen, Jesin Zakaria, Bing Hu, Thanawin Rakthanmanon, Eamonn J. Keogh:
Towards never-ending learning from time series streams. KDD 2013: 874-882 - [c119]Bing Hu, Yanping Chen, Eamonn J. Keogh:
Time Series Classification under More Realistic Assumptions. SDM 2013: 578-586 - [c118]Eamonn J. Keogh, Thanawin Rakthanmanon:
Fast Shapelets: A Scalable Algorithm for Discovering Time Series Shapelets. SDM 2013: 668-676 - 2012
- [j36]Thanawin Rakthanmanon, Qiang Zhu, Eamonn J. Keogh:
Efficiently Finding Near Duplicate Figures in Archives of Historical Documents. J. Multim. 7(2): 109-123 (2012) - [j35]Qiang Zhu, Eamonn J. Keogh:
Mining historical manuscripts with local color patches. Knowl. Inf. Syst. 30(3): 637-665 (2012) - [j34]Thanawin Rakthanmanon, Eamonn J. Keogh, Stefano Lonardi, Scott Evans:
MDL-based time series clustering. Knowl. Inf. Syst. 33(2): 371-399 (2012) - [c117]Mahbub Hasan, Abdullah Mueen, Vassilis J. Tsotras, Eamonn J. Keogh:
Diversifying query results on semi-structured data. CIKM 2012: 2099-2103 - [c116]Jesin Zakaria, Abdullah Mueen, Eamonn J. Keogh:
Clustering Time Series Using Unsupervised-Shapelets. ICDM 2012: 785-794 - [c115]Thanawin Rakthanmanon, Bilson J. L. Campana, Abdullah Mueen, Gustavo E. A. P. A. Batista, M. Brandon Westover, Qiang Zhu, Jesin Zakaria, Eamonn J. Keogh:
Searching and mining trillions of time series subsequences under dynamic time warping. KDD 2012: 262-270 - [c114]Jesin Zakaria, Sarah Rotschafer, Abdullah Mueen, Khaleel Razak, Eamonn J. Keogh:
Mining Massive Archives of Mice Sounds with Symbolized Representations. SDM 2012: 588-599 - [c113]Yuan Hao, Bilson J. L. Campana, Eamonn J. Keogh:
Monitoring and Mining Insect Sounds in Visual Space. SDM 2012: 792-803 - [c112]Bing Hu, Thanawin Rakthanmanon, Bilson J. L. Campana, Abdullah Mueen, Eamonn J. Keogh:
Image Mining of Historical Manuscripts to Establish Provenance. SDM 2012: 804-815 - [c111]Qiang Zhu, Gustavo E. A. P. A. Batista, Thanawin Rakthanmanon, Eamonn J. Keogh:
A Novel Approximation to Dynamic Time Warping allows Anytime Clustering of Massive Time Series Datasets. SDM 2012: 999-1010 - [c110]Eamonn J. Keogh:
Getting your acceptance rate to 80%: a checklist for publishing. SIGMOD/PODS PhD Symposium 2012: 1-2 - 2011
- [j33]Abdullah Mueen, Eamonn J. Keogh, Qiang Zhu, Sydney Cash, M. Brandon Westover, Nima Bigdely Shamlo:
A disk-aware algorithm for time series motif discovery. Data Min. Knowl. Discov. 22(1-2): 73-105 (2011) - [j32]Lexiang Ye, Eamonn J. Keogh:
Time series shapelets: a novel technique that allows accurate, interpretable and fast classification. Data Min. Knowl. Discov. 22(1-2): 149-182 (2011) - [j31]Qiang Zhu, Xiaoyue Wang, Eamonn J. Keogh, Sang-Hee Lee:
An efficient and effective similarity measure to enable data mining of petroglyphs. Data Min. Knowl. Discov. 23(1): 91-127 (2011) - [c109]Bing Hu, Thanawin Rakthanmanon, Yuan Hao, Scott Evans, Stefano Lonardi, Eamonn J. Keogh:
Towards Discovering the Intrinsic Cardinality and Dimensionality of Time Series Using MDL. Algorithmic Probability and Friends 2011: 184-197 - [c108]Thanawin Rakthanmanon, Qiang Zhu, Eamonn J. Keogh:
Searching historical manuscripts for near-duplicate figures. HIP@ICDAR 2011: 14-21 - [c107]Thanawin Rakthanmanon, Eamonn J. Keogh, Stefano Lonardi, Scott Evans:
Time Series Epenthesis: Clustering Time Series Streams Requires Ignoring Some Data. ICDM 2011: 547-556 - [c106]Thanawin Rakthanmanon, Qiang Zhu, Eamonn J. Keogh:
Mining Historical Documents for Near-Duplicate Figures. ICDM 2011: 557-566 - [c105]Bing Hu, Thanawin Rakthanmanon, Yuan Hao, Scott Evans, Stefano Lonardi, Eamonn J. Keogh:
Discovering the Intrinsic Cardinality and Dimensionality of Time Series Using MDL. ICDM 2011: 1086-1091 - [c104]Gustavo E. A. P. A. Batista, Yuan Hao, Eamonn J. Keogh, Agenor Mafra-Neto:
Towards Automatic Classification on Flying Insects Using Inexpensive Sensors. ICMLA (1) 2011: 364-369 - [c103]Gustavo E. A. P. A. Batista, Eamonn J. Keogh, Agenor Mafra-Neto, Edgar Rowton:
SIGKDD demo: sensors and software to allow computational entomology, an emerging application of data mining. KDD 2011: 761-764 - [c102]Abdullah Mueen, Eamonn J. Keogh, Neal E. Young:
Logical-shapelets: an expressive primitive for time series classification. KDD 2011: 1154-1162 - [c101]Gustavo E. A. P. A. Batista, Xiaoyue Wang, Eamonn J. Keogh:
A Complexity-Invariant Distance Measure for Time Series. SDM 2011: 699-710 - [r7]Eamonn J. Keogh:
Data, Mining Time Series Data. International Encyclopedia of Statistical Science 2011: 339-342 - 2010
- [j30]Xiaoyue Wang, Lexiang Ye, Eamonn J. Keogh, Christian R. Shelton:
Annotating Historical Archives of Images. Int. J. Digit. Libr. Syst. 1(2): 59-80 (2010) - [j29]Bilson J. L. Campana, Eamonn J. Keogh:
A compression-based distance measure for texture. Stat. Anal. Data Min. 3(6): 381-398 (2010) - [j28]Zhengzheng Xing, Jian Pei, Eamonn J. Keogh:
A brief survey on sequence classification. SIGKDD Explor. 12(1): 40-48 (2010) - [c100]Alessandro Camerra, Themis Palpanas, Jin Shieh, Eamonn J. Keogh:
iSAX 2.0: Indexing and Mining One Billion Time Series. ICDM 2010: 58-67 - [c99]Jin Shieh, Eamonn J. Keogh:
Polishing the Right Apple: Anytime Classification Also Benefits Data Streams with Constant Arrival Times. ICDM 2010: 461-470 - [c98]Qiang Zhu, Eamonn J. Keogh:
Mother Fugger: Mining Historical Manuscripts with Local Color Patches. ICDM 2010: 699-708 - [c97]Vit Niennattrakul, Eamonn J. Keogh, Chotirat Ann Ratanamahatana:
Data Editing Techniques to Allow the Application of Distance-Based Outlier Detection to Streams. ICDM 2010: 947-952 - [c96]Doruk Sart, Abdullah Mueen, Walid A. Najjar, Eamonn J. Keogh, Vit Niennattrakul:
Accelerating Dynamic Time Warping Subsequence Search with GPUs and FPGAs. ICDM 2010: 1001-1006 - [c95]Eamonn J. Keogh:
How to Do Good Data Mining Research and Get it Published in Top Venues. ICDM 2010: 1219 - [c94]Gustavo E. A. P. A. Batista, Bilson J. L. Campana, Eamonn J. Keogh:
Classification of Live Moths Combining Texture, Color and Shape Primitives. ICMLA 2010: 903-906 - [c93]Qiang Zhu, Eamonn J. Keogh:
Using CAPTCHAs to Index Cultural Artifacts. IDA 2010: 245-257 - [c92]Abdullah Mueen, Eamonn J. Keogh:
Online discovery and maintenance of time series motifs. KDD 2010: 1089-1098 - [c91]Bilson J. L. Campana, Eamonn J. Keogh:
A Compression Based Distance Measure for Texture. SDM 2010: 850-861 - [p4]Chotirat Ann Ratanamahatana, Jessica Lin, Dimitrios Gunopulos, Eamonn J. Keogh, Michail Vlachos, Gautam Das:
Mining Time Series Data. Data Mining and Knowledge Discovery Handbook 2010: 1049-1077 - [r6]Eamonn J. Keogh, Abdullah Mueen:
Curse of Dimensionality. Encyclopedia of Machine Learning 2010: 257-258 - [r5]Eamonn J. Keogh:
Instance-Based Learning. Encyclopedia of Machine Learning 2010: 549-550 - [r4]Eamonn J. Keogh:
Nearest Neighbor. Encyclopedia of Machine Learning 2010: 714-715 - [r3]Eamonn J. Keogh:
Time Series. Encyclopedia of Machine Learning 2010: 987-988 - [i1]Xiaoyue Wang, Hui Ding, Goce Trajcevski, Peter Scheuermann, Eamonn J. Keogh:
Experimental Comparison of Representation Methods and Distance Measures for Time Series Data. CoRR abs/1012.2789 (2010)
2000 – 2009
- 2009
- [j27]Jin Shieh, Eamonn J. Keogh:
iSAX: disk-aware mining and indexing of massive time series datasets. Data Min. Knowl. Discov. 19(1): 24-57 (2009) - [j26]Eamonn J. Keogh, Li Wei, Xiaopeng Xi, Michail Vlachos, Sang-Hee Lee, Pavlos Protopapas:
Supporting exact indexing of arbitrarily rotated shapes and periodic time series under Euclidean and warping distance measures. VLDB J. 18(3): 611-630 (2009) - [c90]Abdullah Mueen, Eamonn J. Keogh, Nima Bigdely Shamlo:
Finding Time Series Motifs in Disk-Resident Data. ICDM 2009: 367-376 - [c89]Xiaoyue Wang, Eamonn J. Keogh:
Augmenting Historical Manuscripts with Automatic Hyperlinks. ISM 2009: 571-576 - [c88]Xiaoyue Wang, Eamonn J. Keogh:
Finding centuries-old hyperlinks with a novel semi-supervised learning technique. JCDL 2009: 451-452 - [c87]Lexiang Ye, Eamonn J. Keogh:
Time series shapelets: a new primitive for data mining. KDD 2009: 947-956 - [c86]Qiang Zhu, Xiaoyue Wang, Eamonn J. Keogh, Sang-Hee Lee:
Augmenting the generalized hough transform to enable the mining of petroglyphs. KDD 2009: 1057-1066 - [c85]Lexiang Ye, Xiaoyue Wang, Eamonn J. Keogh, Agenor Mafra-Neto:
Autocannibalistic and Anyspace Indexing Algorithms with Application to Sensor Data Mining. SDM 2009: 85-96 - [c84]Abdullah Mueen, Eamonn J. Keogh, Qiang Zhu, Sydney Cash, M. Brandon Westover:
Exact Discovery of Time Series Motifs. SDM 2009: 473-484 - [r2]Eamonn J. Keogh, Li Keogh, John C. Handley:
Compression-Based Data Mining. Encyclopedia of Data Warehousing and Mining 2009: 278-285 - 2008
- [j25]Li Wei, Eamonn J. Keogh, Xiaopeng Xi, Melissa Yoder:
Efficiently finding unusual shapes in large image databases. Data Min. Knowl. Discov. 17(3): 343-376 (2008) - [j24]Dragomir Yankov, Eamonn J. Keogh, Umaa Rebbapragada:
Disk aware discord discovery: finding unusual time series in terabyte sized datasets. Knowl. Inf. Syst. 17(2): 241-262 (2008) - [j23]Xiaopeng Xi, Ken Ueno, Eamonn J. Keogh, Dah-Jye Lee:
Converting non-parametric distance-based classification to anytime algorithms. Pattern Anal. Appl. 11(3-4): 321-336 (2008) - [j22]Hui Ding, Goce Trajcevski, Peter Scheuermann, Xiaoyue Wang, Eamonn J. Keogh:
Querying and mining of time series data: experimental comparison of representations and distance measures. Proc. VLDB Endow. 1(2): 1542-1552 (2008) - [j21]Themis Palpanas, Michail Vlachos, Eamonn J. Keogh, Dimitrios Gunopulos:
Streaming Time Series Summarization Using User-Defined Amnesic Functions. IEEE Trans. Knowl. Data Eng. 20(7): 992-1006 (2008) - [j20]Dragomir Yankov, Eamonn J. Keogh, Li Wei, Xiaopeng Xi, Wendy L. Hodges:
Fast Best-Match Shape Searching in Rotation-Invariant Metric Spaces. IEEE Trans. Multim. 10(2): 230-239 (2008) - [j19]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. VLDB J. 17(4): 899-921 (2008) - [c83]Shashwati Kasetty, Candice Stafford, Gregory P. Walker, Xiaoyue Wang, Eamonn J. Keogh:
Real-Time Classification of Streaming Sensor Data. ICTAI (1) 2008: 149-156 - [c82]Xiaoyue Wang, Lexiang Ye, Eamonn J. Keogh, Christian R. Shelton:
Annotating historical archives of images. JCDL 2008: 341-350 - [c81]Jin Shieh, Eamonn J. Keogh:
iSAX: indexing and mining terabyte sized time series. KDD 2008: 623-631 - [c80]Lexiang Ye, Xiaoyue Wang, Dragomir Yankov, Eamonn J. Keogh:
The Asymmetric Approximate Anytime Join: A New Primitive with Applications to Data Mining. SDM 2008: 363-374 - [p3]Xiaozhe Wang, Eamonn J. Keogh:
A Clustering Analysis for Target Group Identification by Locality in Motor Insurance Industry. Soft Computing Applications in Business 2008: 113-127 - [r1]Eamonn J. Keogh:
Indexing and Mining Time Series Data. Encyclopedia of GIS 2008: 493-497 - 2007
- [j18]Eamonn J. Keogh, Stefano Lonardi, Chotirat Ann Ratanamahatana, Li Wei, Sang-Hee Lee, John C. Handley:
Compression-based data mining of sequential data. Data Min. Knowl. Discov. 14(1): 99-129 (2007) - [j17]Jessica Lin, Eamonn J. Keogh, Li Wei, Stefano Lonardi:
Experiencing SAX: a novel symbolic representation of time series. Data Min. Knowl. Discov. 15(2): 107-144 (2007) - [j16]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:
Domain-Driven, Actionable Knowledge Discovery. IEEE Intell. Syst. 22(4): 78-88 (2007) - [j15]Eamonn J. Keogh, Jessica Lin, Sang-Hee Lee, Helga Van Herle:
Finding the most unusual time series subsequence: algorithms and applications. Knowl. Inf. Syst. 11(1): 1-27 (2007) - [j14]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. Knowl. Inf. Syst. 11(3): 313-344 (2007) - [c79]Petko Bakalov, Eamonn J. Keogh, Vassilis J. Tsotras:
TS2-tree - an efficient similarity based organization for trajectory data. GIS 2007: 58 - [c78]Dragomir Yankov, Eamonn J. Keogh, Umaa Rebbapragada:
Disk Aware Discord Discovery: Finding Unusual Time Series in Terabyte Sized Datasets. ICDM 2007: 381-390 - [c77]Dragomir Yankov, Eamonn J. Keogh, Kin Fai Kan:
Locally Constrained Support Vector Clustering. ICDM 2007: 715-720 - [c76]Dragomir Yankov, Eamonn J. Keogh, Jose Medina, Bill Yuan-chi Chiu, Victor B. Zordan:
Detecting time series motifs under uniform scaling. KDD 2007: 844-853 - [c75]Michail Vlachos, Bahar Taneri, Eamonn J. Keogh, Philip S. Yu:
Visual Exploration of Genomic Data. PKDD 2007: 613-620 - [c74]Xiaopeng Xi, Eamonn J. Keogh, Li Wei, Agenor Mafra-Neto:
Finding Motifs in a Database of Shapes. SDM 2007: 249-260 - [c73]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. SDM 2007: 449-454 - [c72]Dragomir Yankov, Eamonn J. Keogh, Li Wei, Xiaopeng Xi, Wendy L. Hodges:
Fast Best-Match Shape Searching in Rotation Invariant Metric Spaces. SDM 2007: 611-616 - 2006
- [j13]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. Data Min. Knowl. Discov. 13(1): 11-40 (2006) - [j12]Stefano Lonardi, Jessica Lin, Eamonn J. Keogh, Bill Yuan-chi Chiu:
Efficient Discovery of Unusual Patterns in Time Series. New Gener. Comput. 25(1): 61-93 (2006) - [j11]Eamonn J. Keogh, Jessica Lin, Ada Wai-Chee Fu, Helga Van Herle:
Finding Unusual Medical Time-Series Subsequences: Algorithms and Applications. IEEE Trans. Inf. Technol. Biomed. 10(3): 429-439 (2006) - [j10]Michail Vlachos, Marios Hadjieleftheriou, Dimitrios Gunopulos, Eamonn J. Keogh:
Indexing Multidimensional Time-Series. VLDB J. 15(1): 1-20 (2006) - [c71]Ada Wai-Chee Fu, Oscar Tat-Wing Leung, Eamonn J. Keogh, Jessica Lin:
Finding Time Series Discords Based on Haar Transform. ADMA 2006: 31-41 - [c70]Pablo Viana, Ann Gordon-Ross, Eamonn J. Keogh, Edna Barros, Frank Vahid:
Configurable cache subsetting for fast cache tuning. DAC 2006: 695-700 - [c69]Dragomir Yankov, Dennis DeCoste, Eamonn J. Keogh:
Ensembles of Nearest Neighbor Forecasts. ECML 2006: 545-556 - [c68]Li Wei, John C. Handley, Nathaniel Martin, Tong Sun, Eamonn J. Keogh:
Clustering Workflow Requirements Using Compression Dissimilarity Measure. ICDM Workshops 2006: 50-54 - [c67]Ken Ueno, Xiaopeng Xi, Eamonn J. Keogh, Dah-Jye Lee:
Anytime Classification Using the Nearest Neighbor Algorithm with Applications to Stream Mining. ICDM 2006: 623-632 - [c66]Li Wei, Eamonn J. Keogh, Xiaopeng Xi:
SAXually Explicit Images: Finding Unusual Shapes. ICDM 2006: 711-720 - [c65]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. ICDM 2006: 912-916 - [c64]Dragomir Yankov, Eamonn J. Keogh:
Manifold Clustering of Shapes. ICDM 2006: 1167-1171 - [c63]Xiaopeng Xi, Eamonn J. Keogh, Christian R. Shelton, Li Wei, Chotirat Ann Ratanamahatana:
Fast time series classification using numerosity reduction. ICML 2006: 1033-1040 - [c62]Aris Anagnostopoulos, Michail Vlachos, Marios Hadjieleftheriou, Eamonn J. Keogh, Philip S. Yu:
Global distance-based segmentation of trajectories. KDD 2006: 34-43 - [c61]Li Wei, Eamonn J. Keogh:
Semi-supervised time series classification. KDD 2006: 748-753 - [c60]Eamonn J. Keogh:
Data mining and information retrieval in time series/multimedia databases. ACM Multimedia 2006: 10 - [c59]Jessica Lin, Eamonn J. Keogh:
Group SAX: Extending the Notion of Contrast Sets to Time Series and Multimedia Data. PKDD 2006: 284-296 - [c58]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. VLDB 2006: 882-893 - [c57]Eamonn J. Keogh:
A Decade of Progress in Indexing and Mining Large Time Series Databases. VLDB 2006: 1268 - 2005
- [j9]Jessica Lin, Eamonn J. Keogh, Stefano Lonardi:
Visualizing and discovering non-trivial patterns in large time series databases. Inf. Vis. 4(2): 61-82 (2005) - [j8]Li Wei, Eamonn J. Keogh, Xiaopeng Xi, Stefano Lonardi:
Integrating Lite-Weight but Ubiquitous Data Mining into GUI Operating Systems. J. Univers. Comput. Sci. 11(11): 1820-1834 (2005) - [j7]Eamonn J. Keogh, Chotirat (Ann) Ratanamahatana:
Exact indexing of dynamic time warping. Knowl. Inf. Syst. 7(3): 358-386 (2005) - [j6]Eamonn J. Keogh, Jessica Lin:
Clustering of time-series subsequences is meaningless: implications for previous and future research. Knowl. Inf. Syst. 8(2): 154-177 (2005) - [j5]Eamonn J. Keogh:
Guest Editorial. Mach. Learn. 58(2-3): 103-105 (2005) - [c56]Jessica Lin, Eamonn J. Keogh, Ada Wai-Chee Fu, Helga Van Herle:
Approximations to Magic: Finding Unusual Medical Time Series. CBMS 2005: 329-334 - [c55]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. CBMS 2005: 341-346 - [c54]Chotirat (Ann) Ratanamahatana, Eamonn J. Keogh:
Multimedia Retrieval Using Time Series Representation and Relevance Feedback. ICADL 2005: 400-405 - [c53]Eamonn J. Keogh, Jessica Lin, Ada Wai-Chee Fu:
HOT SAX: Efficiently Finding the Most Unusual Time Series Subsequence. ICDM 2005: 226-233 - [c52]Li Wei, Eamonn J. Keogh, Helga Van Herle, Agenor Mafra-Neto:
Atomic Wedgie: Efficient Query Filtering for Streaming Times Series. ICDM 2005: 490-497 - [c51]Longin Jan Latecki, Vasileios Megalooikonomou, Qiang Wang, Rolf Lakämper, Chotirat (Ann) Ratanamahatana, Eamonn J. Keogh:
Partial Elastic Matching of Time Series. ICDM 2005: 701-704 - [c50]Dragomir Yankov, Eamonn J. Keogh, Stefano Lonardi, Ada Wai-Chee Fu:
Dot Plots for Time Series Analysis. ICTAI 2005: 159-168 - [c49]Chotirat (Ann) Ratanamahatana, Eamonn J. Keogh:
Using Relevance Feedback to Learn Both the Distance Measure and the Query in Multimedia Databases. KES (2) 2005: 16-23 - [c48]Petko Bakalov, Marios Hadjieleftheriou, Eamonn J. Keogh, Vassilis J. Tsotras:
Efficient trajectory joins using symbolic representations. Mobile Data Management 2005: 86-93 - [c47]Jessica Lin, Michail Vlachos, Eamonn J. Keogh, Dimitrios Gunopulos, Jian-Wei Liu, Shou-Jian Yu, Jia-Jin Le:
A MPAA-Based Iterative Clustering Algorithm Augmented by Nearest Neighbors Search for Time-Series Data Streams. PAKDD 2005: 333-342 - [c46]Chotirat (Ann) Ratanamahatana, Eamonn J. Keogh, Anthony J. Bagnall, Stefano Lonardi:
A Novel Bit Level Time Series Representation with Implication of Similarity Search and Clustering. PAKDD 2005: 771-777 - [c45]Eamonn J. Keogh:
Recent Advances in Mining Time Series Data. PKDD 2005: 6 - [c44]Longin Jan Latecki, Vasilis Megalooikonomou, Qiang Wang, Rolf Lakämper, Chotirat (Ann) Ratanamahatana, Eamonn J. Keogh:
Elastic Partial Matching of Time Series. PKDD 2005: 577-584 - [c43]Chotirat (Ann) Ratanamahatana, Eamonn J. Keogh:
Three Myths about Dynamic Time Warping Data Mining. SDM 2005: 506-510 - [c42]Nitin Kumar, Venkata Nishanth Lolla, Eamonn J. Keogh, Stefano Lonardi, Chotirat (Ann) Ratanamahatana:
Time-series Bitmaps: a Practical Visualization Tool for Working with Large Time Series Databases. SDM 2005: 531-535 - [c41]Li Wei, Nitin Kumar, Venkata Nishanth Lolla, Eamonn J. Keogh, Stefano Lonardi, Chotirat (Ann) Ratanamahatana:
Assumption-Free Anomaly Detection in Time Series. SSDBM 2005: 237-240 - [c40]Eamonn J. Keogh:
Visualization and Mining of Temporal Data. IEEE Visualization 2005: 126 - [c39]Ada Wai-Chee Fu, Eamonn J. Keogh, Leo Yung Hang Lau, Chotirat (Ann) Ratanamahatana:
Scaling and Time Warping in Time Series Querying. VLDB 2005: 649-660 - [p2]Michail Vlachos, Marios Hadjieleftheriou, Eamonn J. Keogh, Dimitrios Gunopulos:
Indexing Multi-Dimensional Trajectories for Similarity Queries. Spatial Databases 2005: 107-128 - [p1]Chotirat (Ann) Ratanamahatana, Jessica Lin, Dimitrios Gunopulos, Eamonn J. Keogh, Michail Vlachos, Gautam Das:
Mining Time Series Data. The Data Mining and Knowledge Discovery Handbook 2005: 1069-1103 - 2004
- [c38]Eamonn J. Keogh, Jessica Lin, Stefano Lonardi, Bill Yuan-chi Chiu:
We Have Seen the Future, and It Is Symbolic. ACSW 2004: 83 - [c37]Jessica Lin, Michail Vlachos, Eamonn J. Keogh, Dimitrios Gunopulos:
Iterative Incremental Clustering of Time Series. EDBT 2004: 106-122 - [c36]Themistoklis Palpanas, Michail Vlachos, Eamonn J. Keogh, Dimitrios Gunopulos, Wagner Truppel:
Online Amnesic Approximation of Streaming Time Series. ICDE 2004: 339-349 - [c35]Eamonn J. Keogh, Stefano Lonardi, Chotirat (Ann) Ratanamahatana:
Towards parameter-free data mining. KDD 2004: 206-215 - [c34]Jessica Lin, Eamonn J. Keogh, Stefano Lonardi, Jeffrey P. Lankford, Donna M. Nystrom:
Visually mining and monitoring massive time series. KDD 2004: 460-469 - [c33]Chotirat (Ann) Ratanamahatana, Eamonn J. Keogh:
Making Time-Series Classification More Accurate Using Learned Constraints. SDM 2004: 11-22 - [c32]Eamonn J. Keogh, Themis Palpanas, Victor B. Zordan, Dimitrios Gunopulos, Marc Cardle:
Indexing Large Human-Motion Databases. VLDB 2004: 780-791 - [c31]Jessica Lin, Eamonn J. Keogh, Stefano Lonardi, Jeffrey P. Lankford, Donna M. Nystrom:
VizTree: a Tool for Visually Mining and Monitoring Massive Time Series Databases. VLDB 2004: 1269-1272 - [c30]Jiyuan An, Yi-Ping Phoebe Chen, Eamonn J. Keogh:
A Grid-Based Index Method for Time Warping Distance. WAIM 2004: 65-75 - 2003
- [j4]Eamonn J. Keogh, Shruti Kasetty:
On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration. Data Min. Knowl. Discov. 7(4): 349-371 (2003) - [c29]Jessica Lin, Eamonn J. Keogh, Stefano Lonardi, Bill Yuan-chi Chiu:
A symbolic representation of time series, with implications for streaming algorithms. DMKD 2003: 2-11 - [c28]Jessica Lin, Eamonn J. Keogh, Wagner Truppel:
Clustering of streaming time series is meaningless. DMKD 2003: 56-65 - [c27]Jessica Lin, Eamonn J. Keogh, Wagner Truppel:
(Not) Finding Rules in Time Series: A Surprising Result with Implications for Previous and Future Research. IC-AI 2003: 55-61 - [c26]Eamonn J. Keogh, Jessica Lin, Wagner Truppel:
Clustering of Time Series Subsequences is Meaningless: Implications for Previous and Future Research. ICDM 2003: 115-122 - [c25]Jiyuan An, Hanxiong Chen, Kazutaka Furuse, Nobuo Ohbo, Eamonn J. Keogh:
Grid-Based Indexing for Large Time Series Databases. IDEAL 2003: 614-621 - [c24]Michail Vlachos, Marios Hadjieleftheriou, Dimitrios Gunopulos, Eamonn J. Keogh:
Indexing multi-dimensional time-series with support for multiple distance measures. KDD 2003: 216-225 - [c23]Bill Yuan-chi Chiu, Eamonn J. Keogh, Stefano Lonardi:
Probabilistic discovery of time series motifs. KDD 2003: 493-498 - [c22]Eamonn J. Keogh:
Efficiently Finding Arbitrarily Scaled Patterns in Massive Time Series Databases. PKDD 2003: 253-265 - [c21]Eamonn J. Keogh:
A Gentle Introduction to Machine Learning and Data Mining for the Database Community. SBBD 2003: 2 - 2002
- [j3]Eamonn J. Keogh, Michael J. Pazzani:
Learning the Structure of Augmented Bayesian Classifiers. Int. J. Artif. Intell. Tools 11(4): 587-601 (2002) - [j2]Kaushik Chakrabarti, Eamonn J. Keogh, Sharad Mehrotra, Michael J. Pazzani:
Locally adaptive dimensionality reduction for indexing large time series databases. ACM Trans. Database Syst. 27(2): 188-228 (2002) - [c20]Eamonn J. Keogh, Harry Hochheiser, Ben Shneiderman:
An Augmented Visual Query Mechanism for Finding Patterns in Time Series Data. FQAS 2002: 240-250 - [c19]Pranav Patel, Eamonn J. Keogh, Jessica Lin, Stefano Lonardi:
Mining Motifs in Massive Time Series Databases. ICDM 2002: 370-377 - [c18]Eamonn J. Keogh, Shruti Kasetty:
On the need for time series data mining benchmarks: a survey and empirical demonstration. KDD 2002: 102-111 - [c17]Eamonn J. Keogh, Stefano Lonardi, Bill Yuan-chi Chiu:
Finding surprising patterns in a time series database in linear time and space. KDD 2002: 550-556 - [c16]Eamonn J. Keogh:
Indexing and Mining Time Series. SBBD 2002: 9 - [c15]Selina Chu, Eamonn J. Keogh, David M. Hart, Michael J. Pazzani:
Iterative Deepening Dynamic Time Warping for Time Series. SDM 2002: 195-212 - [c14]Eamonn J. Keogh:
Exact Indexing of Dynamic Time Warping. VLDB 2002: 406-417 - 2001
- [j1]Eamonn J. Keogh, Kaushik Chakrabarti, Michael J. Pazzani, Sharad Mehrotra:
Dimensionality Reduction for Fast Similarity Search in Large Time Series Databases. Knowl. Inf. Syst. 3(3): 263-286 (2001) - [c13]Eamonn J. Keogh, Selina Chu, David M. Hart, Michael J. Pazzani:
An Online Algorithm for Segmenting Time Series. ICDM 2001: 289-296 - [c12]Eamonn J. Keogh, Selina Chu, Michael J. Pazzani:
Ensemble-index: a new approach to indexing large databases. KDD 2001: 117-125 - [c11]Eamonn J. Keogh, Michael J. Pazzani:
Derivative Dynamic Time Warping. SDM 2001: 1-11 - [c10]Eamonn J. Keogh, Kaushik Chakrabarti, Sharad Mehrotra, Michael J. Pazzani:
Locally Adaptive Dimensionality Reduction for Indexing Large Time Series Databases. SIGMOD Conference 2001: 151-162 - 2000
- [c9]Eamonn J. Keogh, Michael J. Pazzani:
Scaling up dynamic time warping for datamining applications. KDD 2000: 285-289 - [c8]Eamonn J. Keogh, Michael J. Pazzani:
A Simple Dimensionality Reduction Technique for Fast Similarity Search in Large Time Series Databases. PAKDD 2000: 122-133
1990 – 1999
- 1999
- [c7]Eamonn J. Keogh, Michael J. Pazzani:
Learning augmented Bayesian classifiers: A comparison of distribution-based and classification-based approaches. AISTATS 1999 - [c6]Eamonn J. Keogh, Michael J. Pazzani:
Scaling up Dynamic Time Warping to Massive Dataset. PKDD 1999: 1-11 - [c5]Eamonn J. Keogh, Michael J. Pazzani:
Relevance Feedback Retrieval of Time Series Data. SIGIR 1999: 183-190 - [c4]Eamonn J. Keogh, Michael J. Pazzani:
An Indexing Scheme for Fast Similarity Search in Large Time Series Databases. SSDBM 1999: 56-67 - [d1]Eamonn J. Keogh, Michael J. Pazzani:
Pseudo Periodic Synthetic Time Series. UCI Machine Learning Repository, 1999
- 1998
- [c3]Eamonn J. Keogh, Michael J. Pazzani:
An Enhanced Representation of Time Series Which Allows Fast and Accurate Classification, Clustering and Relevance Feedback. KDD 1998: 239-243 - 1997
- [c2]Eamonn J. Keogh:
Fast Similarity Search in the Presence of Longitudinal Scaling in Time Series Databases. ICTAI 1997: 578-584 - [c1]Eamonn J. Keogh, Padhraic Smyth:
A Probabilistic Approach to Fast Pattern Matching in Time Series Databases. KDD 1997: 24-30
Coauthor Index
Gustavo Batista
aka: Gustavo E. A. P. A. Batista
aka: Gustavo E. A. P. A. Batista
Themis Palpanas
aka: Themistoklis Palpanas
aka: Themistoklis Palpanas
Chotirat (Ann) Ratanamahatana
aka: Chotirat Ann Ratanamahatana
aka: Chotirat Ann Ratanamahatana
Zachary Schall-Zimmerman
aka: Zachary Zimmerman
aka: Zachary Zimmerman
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