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Eamonn 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) - [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 - [i23]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) - 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
aka: Gustavo E. A. P. A. Batista
aka: Themistoklis Palpanas
aka: Chotirat Ann Ratanamahatana
aka: Zachary Zimmerman
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