| 2011 |
|---|
| 148 | A disk-aware algorithm for time series motif discovery. Abdullah Mueen, Eamonn J. Keogh, Qiang Zhu, Sydney Cash, M. Brandon Westover, Nima Bigdely Shamlo. Data Min. Knowl. Discov. (22): 73-105 (2011). Web SearchBibTeXDownload |
| 147 | An efficient and effective similarity measure to enable data mining of petroglyphs. Qiang Zhu, Xiaoyue Wang, Eamonn J. Keogh, Sang-Hee Lee. Data Min. Knowl. Discov. (23): 91-127 (2011). Web SearchBibTeXDownload |
| 146 | Time series shapelets: a novel technique that allows accurate, interpretable and fast classification. Lexiang Ye, Eamonn J. Keogh. Data Min. Knowl. Discov. (22): 149-182 (2011). Web SearchBibTeXDownload |
| 145 | Mining Historical Documents for Near-Duplicate Figures. Thanawin Rakthanmanon, Qiang Zhu, Eamonn J. Keogh. ICDM 2011, 557-566. Web SearchBibTeXDownload |
| 144 | Discovering the Intrinsic Cardinality and Dimensionality of Time Series Using MDL. Bing Hu, Thanawin Rakthanmanon, Yuan Hao, Scott Evans, Stefano Lonardi, Eamonn J. Keogh. ICDM 2011, 1086-1091. Web SearchBibTeXDownload |
| 143 | Time Series Epenthesis: Clustering Time Series Streams Requires Ignoring Some Data. Thanawin Rakthanmanon, Eamonn J. Keogh, Stefano Lonardi, Scott Evans. ICDM 2011, 547-556. Web SearchBibTeXDownload |
| 142 | Logical-shapelets: an expressive primitive for time series classification. Abdullah Mueen, Eamonn J. Keogh, Neal Young. KDD 2011, 1154-1162. Web SearchBibTeXDownload |
| 141 | SIGKDD demo: sensors and software to allow computational entomology, an emerging application of data mining. Gustavo E. A. P. A. Batista, Eamonn J. Keogh, Agenor Mafra-Neto, Edgar Rowton. KDD 2011, 761-764. Web SearchBibTeXDownload |
| 140 | A Complexity-Invariant Distance Measure for Time Series. Gustavo E. A. P. A. Batista, Xiaoyue Wang, Eamonn J. Keogh. SDM 2011, 699-710. Web SearchBibTeXDownload |
| 2010 |
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| 139 | Experimental Comparison of Representation Methods and Distance Measures for Time Series Data. Xiaoyue Wang, Hui Ding, Goce Trajcevski, Peter Scheuermann, Eamonn J. Keogh. CoRR (abs/1012.2789) (2010). Web SearchBibTeXDownload |
| 138 | Mining Time Series Data. Chotirat (Ann) Ratanamahatana, Jessica Lin, Dimitrios Gunopulos, Eamonn J. Keogh, Michail Vlachos, Gautam Das. Data Mining and Knowledge Discovery Handbook 2010, 1049-1077. Cited by 21Web SearchBibTeXDownload |
| 137 | Instance-Based Learning. Eamonn J. Keogh. Encyclopedia of Machine Learning 2010, 549-550. Web SearchBibTeXDownload |
| 136 | Nearest Neighbor. Eamonn J. Keogh. Encyclopedia of Machine Learning 2010, 714-715. Web SearchBibTeXDownload |
| 135 | Time Series. Eamonn J. Keogh. Encyclopedia of Machine Learning 2010, 987-988. Web SearchBibTeXDownload |
| 134 | Curse of Dimensionality. Eamonn J. Keogh, Abdullah Mueen. Encyclopedia of Machine Learning 2010, 257-258. Web SearchBibTeXDownload |
| 133 | Mother Fugger: Mining Historical Manuscripts with Local Color Patches. Qiang Zhu, Eamonn J. Keogh. ICDM 2010, 699-708. Web SearchBibTeXDownload |
| 132 | Accelerating Dynamic Time Warping Subsequence Search with GPUs and FPGAs. Doruk Sart, Abdullah Mueen, Walid A. Najjar, Eamonn J. Keogh, Vit Niennattrakul. ICDM 2010, 1001-1006. Web SearchBibTeXDownload |
| 131 | Polishing the Right Apple: Anytime Classification Also Benefits Data Streams with Constant Arrival Times. Jin Shieh, Eamonn J. Keogh. ICDM 2010, 461-470. Web SearchBibTeXDownload |
| 130 | iSAX 2.0: Indexing and Mining One Billion Time Series. Alessandro Camerra, Themis Palpanas, Jin Shieh, Eamonn J. Keogh. ICDM 2010, 58-67. Web SearchBibTeXDownload |
| 129 | Data Editing Techniques to Allow the Application of Distance-Based Outlier Detection to Streams. Vit Niennattrakul, Eamonn J. Keogh, Chotirat Ann Ratanamahatana. ICDM 2010, 947-952. Web SearchBibTeXDownload |
| 128 | How to Do Good Data Mining Research and Get it Published in Top Venues. Eamonn J. Keogh. ICDM 2010, 1219. Web SearchBibTeXDownload |
| 127 | Classification of Live Moths Combining Texture, Color and Shape Primitives. Gustavo E. A. P. A. Batista, Bilson J. L. Campana, Eamonn J. Keogh. ICMLA 2010, 903-906. Web SearchBibTeXDownload |
| 126 | Using CAPTCHAs to Index Cultural Artifacts. Qiang Zhu, Eamonn J. Keogh. IDA 2010, 245-257. Web SearchBibTeXDownload |
| 125 | Annotating Historical Archives of Images. Xiaoyue Wang, Lexiang Ye, Eamonn J. Keogh, Christian R. Shelton. IJDLS (1): 59-80 (2010). Web SearchBibTeXDownload |
| 124 | Online discovery and maintenance of time series motifs. Abdullah Mueen, Eamonn J. Keogh. KDD 2010, 1089-1098. Web SearchBibTeXDownload |
| 123 | A Compression Based Distance Measure for Texture. Bilson J. L. Campana, Eamonn J. Keogh. SDM 2010, 850-861. Web SearchBibTeXDownload |
| 122 | A brief survey on sequence classification. Zhengzheng Xing, Jian Pei, Eamonn J. Keogh. SIGKDD Explorations (12): 40-48 (2010). Web SearchBibTeXDownload |
| 121 | A compression-based distance measure for texture. Bilson J. L. Campana, Eamonn J. Keogh. Statistical Analysis and Data Mining (3): 381-398 (2010). Web SearchBibTeXDownload |
| 2009 |
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| 120 | iSAX: disk-aware mining and indexing of massive time series datasets. Jin Shieh, Eamonn J. Keogh. Data Min. Knowl. Discov. (19): 24-57 (2009). Web SearchBibTeXDownload |
| 119 | Compression-Based Data Mining. Eamonn J. Keogh, Li Keogh, John Handley. Encyclopedia of Data Warehousing and Mining 2009, 278-285. Web SearchBibTeXDownload |
| 118 | Finding Time Series Motifs in Disk-Resident Data. Abdullah Mueen, Eamonn J. Keogh, Nima Bigdely Shamlo. ICDM 2009, 367-376. Web SearchBibTeXDownload |
| 117 | Augmenting Historical Manuscripts with Automatic Hyperlinks. Xiaoyue Wang, Eamonn J. Keogh. ISM 2009, 571-576. Web SearchBibTeXDownload |
| 116 | Finding centuries-old hyperlinks with a novel semi-supervised learning technique. Xiaoyue Wang, Eamonn J. Keogh. JCDL 2009, 451-452. Web SearchBibTeXDownload |
| 115 | Augmenting the generalized hough transform to enable the mining of petroglyphs. Qiang Zhu, Xiaoyue Wang, Eamonn J. Keogh, Sang-Hee Lee. KDD 2009, 1057-1066. Web SearchBibTeXDownload |
| 114 | Time series shapelets: a new primitive for data mining. Lexiang Ye, Eamonn J. Keogh. KDD 2009, 947-956. Web SearchBibTeXDownload |
| 113 | Autocannibalistic and Anyspace Indexing Algorithms with Application to Sensor Data Mining. Lexiang Ye, Xiaoyue Wang, Eamonn J. Keogh, Agenor Mafra-Neto. SDM 2009, 85-96. Web SearchBibTeXDownload |
| 112 | Exact Discovery of Time Series Motifs. Abdullah Mueen, Eamonn J. Keogh, Qiang Zhu, Sydney Cash, M. Brandon Westover. SDM 2009, 473-484. Cited by 2Web SearchBibTeXDownload |
| 111 | Supporting exact indexing of arbitrarily rotated shapes and periodic time series under Euclidean and warping distance measures. Eamonn J. Keogh, Li Wei, Xiaopeng Xi, Michail Vlachos, Sang-Hee Lee, Pavlos Protopapas. VLDB J. (18): 611-630 (2009). Web SearchBibTeXDownload |
| 2008 |
|---|
| 110 | Efficiently finding unusual shapes in large image databases. Li Wei, Eamonn J. Keogh, Xiaopeng Xi, Melissa Yoder. Data Min. Knowl. Discov. (17): 343-376 (2008). Web SearchBibTeXDownload |
| 109 | Indexing and Mining Time Series Data. Eamonn J. Keogh. Encyclopedia of GIS 2008, 493-497. Cited by 14Web SearchBibTeXDownload |
| 108 | Real-Time Classification of Streaming Sensor Data. Shashwati Kasetty, Candice Stafford, Gregory P. Walker, Xiaoyue Wang, Eamonn J. Keogh. ICTAI (1) 2008, 149-156. Cited by 1Web SearchBibTeXDownload |
| 107 | Fast Best-Match Shape Searching in Rotation-Invariant Metric Spaces. Dragomir Yankov, Eamonn J. Keogh, Li Wei, Xiaopeng Xi, Wendy L. Hodges. IEEE Transactions on Multimedia (10): 230-239 (2008). Cited by 1Web SearchBibTeXDownload |
| 106 | Streaming Time Series Summarization Using User-Defined Amnesic Functions. Themis Palpanas, Michail Vlachos, Eamonn J. Keogh, Dimitrios Gunopulos. IEEE Trans. Knowl. Data Eng. (20): 992-1006 (2008). Cited by 4Web SearchBibTeXDownload |
| 105 | Annotating historical archives of images. Xiaoyue Wang, Lexiang Ye, Eamonn J. Keogh, Christian R. Shelton. JCDL 2008, 341-350. Web SearchBibTeXDownload |
| 104 | iSAX: indexing and mining terabyte sized time series. Jin Shieh, Eamonn J. Keogh. KDD 2008, 623-631. Cited by 5Web SearchBibTeXDownload |
| 103 | Disk aware discord discovery: finding unusual time series in terabyte sized datasets. Dragomir Yankov, Eamonn J. Keogh, Umaa Rebbapragada. Knowl. Inf. Syst. (17): 241-262 (2008). Cited by 14Web SearchBibTeXDownload |
| 102 | Converting non-parametric distance-based classification to anytime algorithms. Xiaopeng Xi, Ken Ueno, Eamonn J. Keogh, Dah-Jye Lee. Pattern Anal. Appl. (11): 321-336 (2008). Web SearchBibTeXDownload |
| 101 | Querying and mining of time series data: experimental comparison of representations and distance measures. Hui Ding, Goce Trajcevski, Peter Scheuermann, Xiaoyue Wang, Eamonn J. Keogh. PVLDB (1): 1542-1552 (2008). Cited by 21Web SearchBibTeXDownload |
| 100 | The Asymmetric Approximate Anytime Join: A New Primitive with Applications to Data Mining. Lexiang Ye, Xiaoyue Wang, Dragomir Yankov, Eamonn J. Keogh. SDM 2008, 363-374. Cited by 1Web SearchBibTeXDownload |
| 99 | A Clustering Analysis for Target Group Identification by Locality in Motor Insurance Industry. Xiaozhe Wang, Eamonn J. Keogh. Soft Computing Applications in Business 2008, 113-127. Web SearchBibTeXDownload |
| 98 | Scaling and time warping in time series querying. Ada Wai-Chee Fu, Eamonn J. Keogh, Leo Yung Hang Lau, Chotirat (Ann) Ratanamahatana, Raymond Chi-Wing Wong. VLDB J. (17): 899-921 (2008). Cited by 41Web SearchBibTeXDownload |
| 2007 |
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| 97 | Compression-based data mining of sequential data. Eamonn J. Keogh, Stefano Lonardi, Chotirat Ann Ratanamahatana, Li Wei, Sang-Hee Lee, John Handley. Data Min. Knowl. Discov. (14): 99-129 (2007). Cited by 8Web SearchBibTeXDownload |
| 96 | Experiencing SAX: a novel symbolic representation of time series. Jessica Lin, Eamonn J. Keogh, Li Wei, Stefano Lonardi. Data Min. Knowl. Discov. (15): 107-144 (2007). Cited by 28Web SearchBibTeXDownload |
| 95 | TS2-tree - an efficient similarity based organization for trajectory data. Petko Bakalov, Eamonn J. Keogh, Vassilis J. Tsotras. GIS 2007, 58. Cited by 2Web SearchBibTeXDownload |
| 94 | Disk Aware Discord Discovery: Finding Unusual Time Series in Terabyte Sized Datasets. Dragomir Yankov, Eamonn J. Keogh, Umaa Rebbapragada. ICDM 2007, 381-390. Web SearchBibTeXDownload |
| 93 | Locally Constrained Support Vector Clustering. Dragomir Yankov, Eamonn J. Keogh, Kin Fai Kan. ICDM 2007, 715-720. Cited by 4Web SearchBibTeXDownload |
| 92 | Domain-Driven, Actionable Knowledge Discovery. Longbing Cao, Chengqi Zhang, Qiang Yang, David Bell, Michail Vlachos, Bahar Taneri, Eamonn J. Keogh, Philip S. Yu, Ning Zhong, Mafruz Zaman Ashrafi, David Taniar, Eugene Dubossarsky, Warwick Graco. IEEE Intelligent Systems (22): 78-88 (2007). Web SearchBibTeXDownload |
| 91 | Detecting time series motifs under uniform scaling. Dragomir Yankov, Eamonn J. Keogh, Jose Medina, Bill Yuan-chi Chiu, Victor B. Zordan. KDD 2007, 844-853. Cited by 22Web SearchBibTeXDownload |
| 90 | Finding the most unusual time series subsequence: algorithms and applications. Eamonn J. Keogh, Jessica Lin, Sang-Hee Lee, Helga Van Herle. Knowl. Inf. Syst. (11): 1-27 (2007). Cited by 12Web SearchBibTeXDownload |
| 89 | Efficient query filtering for streaming time series with applications to semisupervised learning of time series classifiers. Li Wei, Eamonn J. Keogh, Helga Van Herle, Agenor Mafra-Neto, Russell J. Abbott. Knowl. Inf. Syst. (11): 313-344 (2007). Cited by 5Web SearchBibTeXDownload |
| 88 | Visual Exploration of Genomic Data. Michail Vlachos, Bahar Taneri, Eamonn J. Keogh, Philip S. Yu. PKDD 2007, 613-620. Web SearchBibTeXDownload |
| 87 | WAT: Finding Top-K Discords in Time Series Database. Yingyi Bu, Oscar Tat-Wing Leung, Ada Wai-Chee Fu, Eamonn J. Keogh, Jian Pei, Sam Meshkin. SDM 2007. Cited by 10Web SearchBibTeXDownload |
| 86 | Fast Best-Match Shape Searching in Rotation Invariant Metric Spaces. Dragomir Yankov, Eamonn J. Keogh, Li Wei, Xiaopeng Xi, Wendy L. Hodges. SDM 2007. Web SearchBibTeXDownload |
| 85 | Finding Motifs in a Database of Shapes. Xiaopeng Xi, Eamonn J. Keogh, Li Wei, Agenor Mafra-Neto. SDM 2007. Cited by 4Web SearchBibTeXDownload |
| 2006 |
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| 84 | Data mining and information retrieval in time series/multimedia databases. Eamonn J. Keogh. ACM Multimedia 2006, 10. Web SearchBibTeXDownload |
| 83 | Finding Time Series Discords Based on Haar Transform. Ada Wai-Chee Fu, Oscar Tat-Wing Leung, Eamonn J. Keogh, Jessica Lin. ADMA 2006, 31-41. Cited by 10Web SearchBibTeXDownload |
| 82 | Configurable cache subsetting for fast cache tuning. Pablo Viana, Ann Gordon-Ross, Eamonn J. Keogh, Edna Barros, Frank Vahid. DAC 2006, 695-700. Cited by 8Web SearchBibTeXDownload |
| 81 | A Bit Level Representation for Time Series Data Mining with Shape Based Similarity. Anthony J. Bagnall, Chotirat (Ann) Ratanamahatana, Eamonn J. Keogh, Stefano Lonardi, Gareth J. Janacek. Data Min. Knowl. Discov. (13): 11-40 (2006). Cited by 8Web SearchBibTeXDownload |
| 80 | Ensembles of Nearest Neighbor Forecasts. Dragomir Yankov, Dennis DeCoste, Eamonn J. Keogh. ECML 2006, 545-556. Cited by 10Web SearchBibTeXDownload |
| 79 | SAXually Explicit Images: Finding Unusual Shapes. Li Wei, Eamonn J. Keogh, Xiaopeng Xi. ICDM 2006, 711-720. Cited by 20Web SearchBibTeXDownload |
| 78 | Intelligent Icons: Integrating Lite-Weight Data Mining and Visualization into GUI Operating Systems. Eamonn J. Keogh, Li Wei, Xiaopeng Xi, Stefano Lonardi, Jin Shieh, Scott Sirowy. ICDM 2006, 912-916. Cited by 5Web SearchBibTeXDownload |
| 77 | Anytime Classification Using the Nearest Neighbor Algorithm with Applications to Stream Mining. Ken Ueno, Xiaopeng Xi, Eamonn J. Keogh, Dah-Jye Lee. ICDM 2006, 623-632. Cited by 12Web SearchBibTeXDownload |
| 76 | Manifold Clustering of Shapes. Dragomir Yankov, Eamonn J. Keogh. ICDM 2006, 1167-1171. Cited by 10Web SearchBibTeXDownload |
| 75 | Clustering Workflow Requirements Using Compression Dissimilarity Measure. Li Wei, John Handley, Nathaniel Martin, Tong Sun, Eamonn J. Keogh. ICDM Workshops 2006, 50-54. Web SearchBibTeXDownload |
| 74 | Fast time series classification using numerosity reduction. Xiaopeng Xi, Eamonn J. Keogh, Christian R. Shelton, Li Wei, Chotirat Ann Ratanamahatana. ICML 2006, 1033-1040. Cited by 37Web SearchBibTeXDownload |
| 73 | Finding Unusual Medical Time-Series Subsequences: Algorithms and Applications. Eamonn J. Keogh, Jessica Lin, Ada Wai-Chee Fu, Helga Van Herle. IEEE Transactions on Information Technology in Biomedicine (10): 429-439 (2006). Cited by 10Web SearchBibTeXDownload |
| 72 | Global distance-based segmentation of trajectories. Aris Anagnostopoulos, Michail Vlachos, Marios Hadjieleftheriou, Eamonn J. Keogh, Philip S. Yu. KDD 2006, 34-43. Cited by 16Web SearchBibTeXDownload |
| 71 | Semi-supervised time series classification. Li Wei, Eamonn J. Keogh. KDD 2006, 748-753. Cited by 18Web SearchBibTeXDownload |
| 70 | Efficient Discovery of Unusual Patterns in Time Series. Stefano Lonardi, Jessica Lin, Eamonn J. Keogh, Bill Yuan-chi Chiu. New Generation Comput. (25): 61-93 (2006). Cited by 1Web SearchBibTeXDownload |
| 69 | Group SAX: Extending the Notion of Contrast Sets to Time Series and Multimedia Data. Jessica Lin, Eamonn J. Keogh. PKDD 2006, 284-296. Cited by 8Web SearchBibTeXDownload |
| 68 | LB_Keogh Supports Exact Indexing of Shapes under Rotation Invariance with Arbitrary Representations and Distance Measures. Eamonn J. Keogh, Li Wei, Xiaopeng Xi, Sang-Hee Lee, Michail Vlachos. VLDB 2006, 882-893. Cited by 55Web SearchBibTeXDownload |
| 67 | A Decade of Progress in Indexing and Mining Large Time Series Databases. Eamonn J. Keogh. VLDB 2006, 1268. Cited by 13Web SearchBibTeXDownload |
| 66 | Indexing Multidimensional Time-Series. Michail Vlachos, Marios Hadjieleftheriou, Dimitrios Gunopulos, Eamonn J. Keogh. VLDB J. (15): 1-20 (2006). Cited by 27Web SearchBibTeXDownload |
| 2005 |
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| 65 | A Practical Tool for Visualizing and Data Mining Medical Time Series. Li Wei, Nitin Kumar, Venkata Nishanth Lolla, Eamonn J. Keogh, Stefano Lonardi, Chotirat (Ann) Ratanamahatana, Helga Van Herle. CBMS 2005, 341-346. Cited by 2Web SearchBibTeXDownload |
| 64 | Approximations to Magic: Finding Unusual Medical Time Series. Jessica Lin, Eamonn J. Keogh, Ada Wai-Chee Fu, Helga Van Herle. CBMS 2005, 329-334. Cited by 14Web SearchBibTeXDownload |
| 63 | Multimedia Retrieval Using Time Series Representation and Relevance Feedback. Chotirat (Ann) Ratanamahatana, Eamonn J. Keogh. ICADL 2005, 400-405. Cited by 2Web SearchBibTeXDownload |
| 62 | HOT SAX: Efficiently Finding the Most Unusual Time Series Subsequence. Eamonn J. Keogh, Jessica Lin, Ada Wai-Chee Fu. ICDM 2005, 226-233. Cited by 84Web SearchBibTeXDownload |
| 61 | Atomic Wedgie: Efficient Query Filtering for Streaming Times Series. Li Wei, Eamonn J. Keogh, Helga Van Herle, Agenor Mafra-Neto. ICDM 2005, 490-497. Cited by 27Web SearchBibTeXDownload |
| 60 | Partial Elastic Matching of Time Series. Longin Jan Latecki, Vasileios Megalooikonomou, Qiang Wang, Rolf Lakämper, Chotirat (Ann) Ratanamahatana, Eamonn J. Keogh. ICDM 2005, 701-704. Web SearchBibTeXDownload |
| 59 | Dot Plots for Time Series Analysis. Dragomir Yankov, Eamonn J. Keogh, Stefano Lonardi, Ada Wai-Chee Fu. ICTAI 2005, 159-168. Cited by 3Web SearchBibTeXDownload |
| 58 | Visualization and Mining of Temporal Data. Eamonn J. Keogh. IEEE Visualization 2005, 126. Web SearchBibTeXDownload |
| 57 | Visualizing and discovering non-trivial patterns in large time series databases. Jessica Lin, Eamonn J. Keogh, Stefano Lonardi. Information Visualization (4): 61-82 (2005). Cited by 29Web SearchBibTeXDownload |
| 56 | Integrating Lite-Weight but Ubiquitous Data Mining into GUI Operating Systems. Li Wei, Eamonn J. Keogh, Xiaopeng Xi, Stefano Lonardi. J. UCS (11): 1820-1834 (2005). Web SearchBibTeXDownload |
| 55 | Using Relevance Feedback to Learn Both the Distance Measure and the Query in Multimedia Databases. Chotirat (Ann) Ratanamahatana, Eamonn J. Keogh. KES (2) 2005, 16-23. Cited by 1Web SearchBibTeXDownload |
| 54 | Clustering of time-series subsequences is meaningless: implications for previous and future research. Eamonn J. Keogh, Jessica Lin. Knowl. Inf. Syst. (8): 154-177 (2005). Cited by 148Web SearchBibTeXDownload |
| 53 | Exact indexing of dynamic time warping. Eamonn J. Keogh, Chotirat (Ann) Ratanamahatana. Knowl. Inf. Syst. (7): 358-386 (2005). Cited by 417Web SearchBibTeXDownload |
| 52 | Guest Editorial. Darrell Conklin, Tadeusz A. Wysocki, Hamid Sharif, L. C. Gundersen, P. P. Leahy, W. Hill, Jyh-Horng Wen, Shiuh-Jeng Wang, Yuh-Ren Tsai, Keh-Ming Lu. Machine Learning (58): 103-105 (2005). Cited by 2Web SearchBibTeXDownload |
| 51 | Efficient trajectory joins using symbolic representations. Petko Bakalov, Marios Hadjieleftheriou, Eamonn J. Keogh, Vassilis J. Tsotras. Mobile Data Management 2005, 86-93. Cited by 23Web SearchBibTeXDownload |
| 50 | A Novel Bit Level Time Series Representation with Implication of Similarity Search and Clustering. Chotirat (Ann) Ratanamahatana, Eamonn J. Keogh, Anthony J. Bagnall, Stefano Lonardi. PAKDD 2005, 771-777. Cited by 22Web SearchBibTeXDownload |
| 49 | A MPAA-Based Iterative Clustering Algorithm Augmented by Nearest Neighbors Search for Time-Series Data Streams. Jessica Lin, Michail Vlachos, Eamonn J. Keogh, Dimitrios Gunopulos, Jian-Wei Liu, Shou-Jian Yu, Jia-Jin Le. PAKDD 2005, 333-342. Web SearchBibTeXDownload |
| 48 | Recent Advances in Mining Time Series Data. Eamonn J. Keogh. PKDD 2005, 6. Cited by 3Web SearchBibTeXDownload |
| 47 | Elastic Partial Matching of Time Series. Longin Jan Latecki, Vasilis Megalooikonomou, Qiang Wang, Rolf Lakämper, Chotirat (Ann) Ratanamahatana, Eamonn J. Keogh. PKDD 2005, 577-584. Cited by 10Web SearchBibTeXDownload |
| 46 | Three Myths about Dynamic Time Warping Data Mining. Chotirat (Ann) Ratanamahatana, Eamonn J. Keogh. SDM 2005. Cited by 63Web SearchBibTeX |
| 45 | Time-series Bitmaps: a Practical Visualization Tool for Working with Large Time Series Databases. Nitin Kumar, Venkata Nishanth Lolla, Eamonn J. Keogh, Stefano Lonardi, Chotirat (Ann) Ratanamahatana. SDM 2005. Cited by 24Web SearchBibTeX |
| 44 | Indexing Multi-Dimensional Trajectories for Similarity Queries. Michail Vlachos, Marios Hadjieleftheriou, Eamonn J. Keogh, Dimitrios Gunopulos. Spatial Databases 2005, 107-128. Web SearchBibTeX |
| 43 | Assumption-Free Anomaly Detection in Time Series. Li Wei, Nitin Kumar, Venkata Nishanth Lolla, Eamonn J. Keogh, Stefano Lonardi, Chotirat (Ann) Ratanamahatana. SSDBM 2005, 237-240. Cited by 16Web SearchBibTeX |
| 42 | Mining Time Series Data. Chotirat (Ann) Ratanamahatana, Jessica Lin, Dimitrios Gunopulos, Eamonn J. Keogh, Michail Vlachos, Gautam Das. The Data Mining and Knowledge Discovery Handbook 2005, 1069-1103. Cited by 21Web SearchBibTeX |
| 41 | Scaling and Time Warping in Time Series Querying. Ada Wai-Chee Fu, Eamonn J. Keogh, Leo Yung Hang Lau, Chotirat (Ann) Ratanamahatana. VLDB 2005, 649-660. Web SearchBibTeXDownload |
| 2004 |
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| 40 | We Have Seen the Future, and It Is Symbolic. Eamonn J. Keogh, Jessica Lin, Stefano Lonardi, Bill Yuan-chi Chiu. ACSW Frontiers 2004, 83. Web SearchBibTeXDownload |
| 39 | Iterative Incremental Clustering of Time Series. Jessica Lin, Michail Vlachos, Eamonn J. Keogh, Dimitrios Gunopulos. EDBT 2004, 106-122. Cited by 51Web SearchBibTeXDownload |
| 38 | Online Amnesic Approximation of Streaming Time Series. Themistoklis Palpanas, Michail Vlachos, Eamonn J. Keogh, Dimitrios Gunopulos, Wagner Truppel. ICDE 2004, 339-349. Cited by 65Web SearchBibTeXDownload |
| 37 | Visually mining and monitoring massive time series. Jessica Lin, Eamonn J. Keogh, Stefano Lonardi, Jeffrey P. Lankford, Donna M. Nystrom. KDD 2004, 460-469. Cited by 51Web SearchBibTeXDownload |
| 36 | Towards parameter-free data mining. Eamonn J. Keogh, Stefano Lonardi, Chotirat (Ann) Ratanamahatana. KDD 2004, 206-215. Cited by 190Web SearchBibTeXDownload |
| 35 | Making Time-Series Classification More Accurate Using Learned Constraints. Chotirat (Ann) Ratanamahatana, Eamonn J. Keogh. SDM 2004. Cited by 73Web SearchBibTeXDownload |
| 34 | VizTree: a Tool for Visually Mining and Monitoring Massive Time Series Databases. Jessica Lin, Eamonn J. Keogh, Stefano Lonardi, Jeffrey P. Lankford, Donna M. Nystrom. VLDB 2004, 1269-1272. Cited by 10Web SearchBibTeXDownload |
| 33 | Indexing Large Human-Motion Databases. Eamonn J. Keogh, Themis Palpanas, Victor B. Zordan, Dimitrios Gunopulos, Marc Cardle. VLDB 2004, 780-791. Cited by 86Web SearchBibTeXDownload |
| 32 | A Grid-Based Index Method for Time Warping Distance. Jiyuan An, Yi-Ping Phoebe Chen, Eamonn J. Keogh. WAIM 2004, 65-75. Web SearchBibTeXDownload |
| 2003 |
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| 31 | On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration. Eamonn J. Keogh, Shruti Kasetty. Data Min. Knowl. Discov. (7): 349-371 (2003). Cited by 355Web SearchBibTeXDownload |
| 30 | Clustering of streaming time series is meaningless. Jessica Lin, Eamonn J. Keogh, Wagner Truppel. DMKD 2003, 56-65. Cited by 20Web SearchBibTeXDownload |
| 29 | A symbolic representation of time series, with implications for streaming algorithms. Jessica Lin, Eamonn J. Keogh, Stefano Lonardi, Bill Yuan-chi Chiu. DMKD 2003, 2-11. Cited by 306Web SearchBibTeXDownload |
| 28 | (Not) Finding Rules in Time Series: A Surprising Result with Implications for Previous and Future Research. Jessica Lin, Eamonn J. Keogh, Wagner Truppel. IC-AI 2003, 55-61. Cited by 2Web SearchBibTeX |
| 27 | Clustering of Time Series Subsequences is Meaningless: Implications for Previous and Future Research. Eamonn J. Keogh, Jessica Lin, Wagner Truppel. ICDM 2003, 115-122. Cited by 2Web SearchBibTeXDownload |
| 26 | Grid-Based Indexing for Large Time Series Databases. Jiyuan An, Hanxiong Chen, Kazutaka Furuse, Nobuo Ohbo, Eamonn J. Keogh. IDEAL 2003, 614-621. Cited by 8Web SearchBibTeXDownload |
| 25 | Probabilistic discovery of time series motifs. Bill Yuan-chi Chiu, Eamonn J. Keogh, Stefano Lonardi. KDD 2003, 493-498. Cited by 147Web SearchBibTeXDownload |
| 24 | Indexing multi-dimensional time-series with support for multiple distance measures. Michail Vlachos, Marios Hadjieleftheriou, Dimitrios Gunopulos, Eamonn J. Keogh. KDD 2003, 216-225. Cited by 147Web SearchBibTeXDownload |
| 23 | Efficiently Finding Arbitrarily Scaled Patterns in Massive Time Series Databases. Eamonn J. Keogh. PKDD 2003, 253-265. Cited by 16Web SearchBibTeXDownload |
| 22 | A Gentle Introduction to Machine Learning and Data Mining for the Database Community. Eamonn J. Keogh. SBBD 2003, 2. Web SearchBibTeX |
| 2002 |
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| 21 | Locally adaptive dimensionality reduction for indexing large time series databases. Kaushik Chakrabarti, Eamonn J. Keogh, Sharad Mehrotra, Michael J. Pazzani. ACM Trans. Database Syst. (27): 188-228 (2002). Cited by 478Web SearchBibTeXDownload |
| 20 | An Augmented Visual Query Mechanism for Finding Patterns in Time Series Data. Eamonn J. Keogh, Harry Hochheiser, Ben Shneiderman. FQAS 2002, 240-250. Cited by 24Web SearchBibTeXDownload |
| 19 | Mining Motifs in Massive Time Series Databases. Pranav Patel, Eamonn J. Keogh, Jessica Lin, Stefano Lonardi. ICDM 2002, 370-377. Cited by 55Web SearchBibTeXDownload |
| 18 | Learning the Structure of Augmented Bayesian Classifiers. Eamonn J. Keogh, Michael J. Pazzani. International Journal on Artificial Intelligence Tools (11): 587-601 (2002). Cited by 31Web SearchBibTeXDownload |
| 17 | Finding surprising patterns in a time series database in linear time and space. Eamonn J. Keogh, Stefano Lonardi, Bill Yuan-chi Chiu. KDD 2002, 550-556. Cited by 168Web SearchBibTeXDownload |
| 16 | On the need for time series data mining benchmarks: a survey and empirical demonstration. Eamonn J. Keogh, Shruti Kasetty. KDD 2002, 102-111. Web SearchBibTeXDownload |
| 15 | Indexing and Mining Time Series. Eamonn J. Keogh. SBBD 2002, 9. Web SearchBibTeX |
| 14 | Iterative Deepening Dynamic Time Warping for Time Series. Selina Chu, Eamonn J. Keogh, David Hart, Michael J. Pazzani. SDM 2002. Cited by 93Web SearchBibTeXDownload |
| 13 | Exact Indexing of Dynamic Time Warping. Eamonn J. Keogh. VLDB 2002, 406-417. Cited by 2Web SearchBibTeXDownload |
| 2001 |
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| 12 | An Online Algorithm for Segmenting Time Series. Eamonn J. Keogh, Selina Chu, David Hart, Michael J. Pazzani. ICDM 2001, 289-296. Cited by 275Web SearchBibTeXDownload |
| 11 | Ensemble-index: a new approach to indexing large databases. Eamonn J. Keogh, Selina Chu, Michael J. Pazzani. KDD 2001, 117-125. Cited by 5Web SearchBibTeXDownload |
| 10 | Dimensionality Reduction for Fast Similarity Search in Large Time Series Databases. Eamonn J. Keogh, Kaushik Chakrabarti, Michael J. Pazzani, Sharad Mehrotra. Knowl. Inf. Syst. (3): 263-286 (2001). Cited by 406Web SearchBibTeXDownload |
| 9 | Locally Adaptive Dimensionality Reduction for Indexing Large Time Series Databases. Eamonn J. Keogh, Kaushik Chakrabarti, Sharad Mehrotra, Michael J. Pazzani. SIGMOD Conference 2001, 151-162. Web SearchBibTeXDownload |
| 2000 |
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| 8 | Scaling up dynamic time warping for datamining applications. Eamonn J. Keogh, Michael J. Pazzani. KDD 2000, 285-289. Cited by 158Web SearchBibTeXDownload |
| 7 | A Simple Dimensionality Reduction Technique for Fast Similarity Search in Large Time Series Databases. Eamonn J. Keogh, Michael J. Pazzani. PAKDD 2000, 122-133. Cited by 3Web SearchBibTeXDownload |
| 1999 |
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| 6 | Scaling up Dynamic Time Warping to Massive Dataset. Eamonn J. Keogh, Michael J. Pazzani. PKDD 1999, 1-11. Cited by 102Web SearchBibTeXDownload |
| 5 | Relevance Feedback Retrieval of Time Series Data. Eamonn J. Keogh, Michael J. Pazzani. SIGIR 1999, 183-190. Cited by 71Web SearchBibTeXDownload |
| 4 | An Indexing Scheme for Fast Similarity Search in Large Time Series Databases. Eamonn J. Keogh, Michael J. Pazzani. SSDBM 1999, 56-67. Cited by 53Web SearchBibTeXDownload |
| 1998 |
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| 3 | An Enhanced Representation of Time Series Which Allows Fast and Accurate Classification, Clustering and Relevance Feedback. Eamonn J. Keogh, Michael J. Pazzani. KDD 1998, 239-243. Cited by 288Web SearchBibTeX |
| 1997 |
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| 2 | Fast Similarity Search in the Presence of Longitudinal Scaling in Time Series Databases. Eamonn J. Keogh. ICTAI 1997, 578-584. Cited by 39Web SearchBibTeXDownload |
| 1 | A Probabilistic Approach to Fast Pattern Matching in Time Series Databases. Eamonn J. Keogh, Padhraic Smyth. KDD 1997, 24-30. Cited by 189Web SearchBibTeX |
