2008 | ||
---|---|---|
109 | EE | Shashwati Kasetty, Candice Stafford, Gregory P. Walker, Xiaoyue Wang, Eamonn J. Keogh: Real-Time Classification of Streaming Sensor Data. ICTAI (1) 2008: 149-156 |
108 | EE | Xiaoyue Wang, Lexiang Ye, Eamonn J. Keogh, Christian R. Shelton: Annotating historical archives of images. JCDL 2008: 341-350 |
107 | EE | Jin Shieh, Eamonn J. Keogh: iSAX: indexing and mining terabyte sized time series. KDD 2008: 623-631 |
106 | EE | 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 |
105 | EE | Eamonn J. Keogh: Indexing and Mining Time Series Data. Encyclopedia of GIS 2008: 493-497 |
104 | EE | 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) |
103 | EE | 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) |
102 | EE | Dragomir Yankov, Eamonn J. Keogh, Li Wei, Xiaopeng Xi, Wendy L. Hodges: Fast Best-Match Shape Searching in Rotation-Invariant Metric Spaces. IEEE Transactions on Multimedia 10(2): 230-239 (2008) |
101 | EE | 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) |
100 | EE | 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. PVLDB 1(2): 1542-1552 (2008) |
99 | EE | 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) |
98 | EE | 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) |
2007 | ||
97 | EE | Petko Bakalov, Eamonn J. Keogh, Vassilis J. Tsotras: TS2-tree - an efficient similarity based organization for trajectory data. GIS 2007: 58 |
96 | EE | Dragomir Yankov, Eamonn J. Keogh, Umaa Rebbapragada: Disk Aware Discord Discovery: Finding Unusual Time Series in Terabyte Sized Datasets. ICDM 2007: 381-390 |
95 | EE | Dragomir Yankov, Eamonn J. Keogh, Kin Fai Kan: Locally Constrained Support Vector Clustering. ICDM 2007: 715-720 |
94 | EE | 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 |
93 | EE | Michail Vlachos, Bahar Taneri, Eamonn J. Keogh, Philip S. Yu: Visual Exploration of Genomic Data. PKDD 2007: 613-620 |
92 | EE | Dragomir Yankov, Eamonn J. Keogh, Li Wei, Xiaopeng Xi, Wendy L. Hodges: Fast Best-Match Shape Searching in Rotation Invariant Metric Spaces. SDM 2007 |
91 | EE | Xiaopeng Xi, Eamonn J. Keogh, Li Wei, Agenor Mafra-Neto: Finding Motifs in a Database of Shapes. SDM 2007 |
90 | EE | 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 |
89 | EE | Eamonn J. Keogh, Stefano Lonardi, Chotirat Ann Ratanamahatana, Li Wei, Sang-Hee Lee, John Handley: Compression-based data mining of sequential data. Data Min. Knowl. Discov. 14(1): 99-129 (2007) |
88 | EE | 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) |
87 | EE | 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: Domain-Driven, Actionable Knowledge Discovery. IEEE Intelligent Systems 22(4): 78-88 (2007) |
86 | EE | 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) |
85 | EE | 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) |
2006 | ||
84 | EE | Eamonn J. Keogh: Data mining and information retrieval in time series/multimedia databases. ACM Multimedia 2006: 10 |
83 | EE | 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 |
82 | EE | Pablo Viana, Ann Gordon-Ross, Eamonn J. Keogh, Edna Barros, Frank Vahid: Configurable cache subsetting for fast cache tuning. DAC 2006: 695-700 |
81 | EE | Dragomir Yankov, Dennis DeCoste, Eamonn J. Keogh: Ensembles of Nearest Neighbor Forecasts. ECML 2006: 545-556 |
80 | EE | Dragomir Yankov, Eamonn J. Keogh: Manifold Clustering of Shapes. ICDM 2006: 1167-1171 |
79 | EE | 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 |
78 | EE | Li Wei, Eamonn J. Keogh, Xiaopeng Xi: SAXually Explicit Images: Finding Unusual Shapes. ICDM 2006: 711-720 |
77 | EE | 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 |
76 | EE | Li Wei, John Handley, Nathaniel Martin, Tong Sun, Eamonn J. Keogh: Clustering Workflow Requirements Using Compression Dissimilarity Measure. ICDM Workshops 2006: 50-54 |
75 | EE | Xiaopeng Xi, Eamonn J. Keogh, Christian R. Shelton, Li Wei, Chotirat Ann Ratanamahatana: Fast time series classification using numerosity reduction. ICML 2006: 1033-1040 |
74 | EE | Aris Anagnostopoulos, Michail Vlachos, Marios Hadjieleftheriou, Eamonn J. Keogh, Philip S. Yu: Global distance-based segmentation of trajectories. KDD 2006: 34-43 |
73 | EE | Li Wei, Eamonn J. Keogh: Semi-supervised time series classification. KDD 2006: 748-753 |
72 | EE | Jessica Lin, Eamonn J. Keogh: Group SAX: Extending the Notion of Contrast Sets to Time Series and Multimedia Data. PKDD 2006: 284-296 |
71 | EE | Eamonn J. Keogh: A Decade of Progress in Indexing and Mining Large Time Series Databases. VLDB 2006: 1268 |
70 | EE | 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 |
69 | EE | 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) |
68 | EE | Eamonn J. Keogh, Jessica Lin, Ada Wai-Chee Fu, Helga Van Herle: Finding Unusual Medical Time-Series Subsequences: Algorithms and Applications. IEEE Transactions on Information Technology in Biomedicine 10(3): 429-439 (2006) |
67 | EE | Stefano Lonardi, Jessica Lin, Eamonn J. Keogh, Bill Yuan-chi Chiu: Efficient Discovery of Unusual Patterns in Time Series. New Generation Comput. 25(1): 61-93 (2006) |
66 | EE | Michail Vlachos, Marios Hadjieleftheriou, Dimitrios Gunopulos, Eamonn J. Keogh: Indexing Multidimensional Time-Series. VLDB J. 15(1): 1-20 (2006) |
2005 | ||
65 | EE | Jessica Lin, Eamonn J. Keogh, Ada Wai-Chee Fu, Helga Van Herle: Approximations to Magic: Finding Unusual Medical Time Series. CBMS 2005: 329-334 |
64 | EE | 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 |
63 | EE | Chotirat (Ann) Ratanamahatana, Eamonn J. Keogh: Multimedia Retrieval Using Time Series Representation and Relevance Feedback. ICADL 2005: 400-405 |
62 | EE | Eamonn J. Keogh, Jessica Lin, Ada Wai-Chee Fu: HOT SAX: Efficiently Finding the Most Unusual Time Series Subsequence. ICDM 2005: 226-233 |
61 | EE | Li Wei, Eamonn J. Keogh, Helga Van Herle, Agenor Mafra-Neto: Atomic Wedgie: Efficient Query Filtering for Streaming Times Series. ICDM 2005: 490-497 |
60 | EE | 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 |
59 | EE | Dragomir Yankov, Eamonn J. Keogh, Stefano Lonardi, Ada Wai-Chee Fu: Dot Plots for Time Series Analysis. ICTAI 2005: 159-168 |
58 | EE | Eamonn J. Keogh: Visualization and Mining of Temporal Data. IEEE Visualization 2005: 126 |
57 | EE | 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 |
56 | EE | Petko Bakalov, Marios Hadjieleftheriou, Eamonn J. Keogh, Vassilis J. Tsotras: Efficient trajectory joins using symbolic representations. Mobile Data Management 2005: 86-93 |
55 | EE | 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 |
54 | EE | 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 |
53 | EE | 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 |
52 | EE | Eamonn J. Keogh: Recent Advances in Mining Time Series Data. PKDD 2005: 6 |
51 | Chotirat (Ann) Ratanamahatana, Eamonn J. Keogh: Three Myths about Dynamic Time Warping Data Mining. SDM 2005 | |
50 | 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 | |
49 | 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 | |
48 | EE | 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 |
47 | Michail Vlachos, Marios Hadjieleftheriou, Eamonn J. Keogh, Dimitrios Gunopulos: Indexing Multi-Dimensional Trajectories for Similarity Queries. Spatial Databases 2005: 107-128 | |
46 | 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 | |
45 | EE | Jessica Lin, Eamonn J. Keogh, Stefano Lonardi: Visualizing and discovering non-trivial patterns in large time series databases. Information Visualization 4(2): 61-82 (2005) |
44 | EE | Li Wei, Eamonn J. Keogh, Xiaopeng Xi, Stefano Lonardi: Integrating Lite-Weight but Ubiquitous Data Mining into GUI Operating Systems. J. UCS 11(11): 1820-1834 (2005) |
43 | EE | Eamonn J. Keogh, Chotirat (Ann) Ratanamahatana: Exact indexing of dynamic time warping. Knowl. Inf. Syst. 7(3): 358-386 (2005) |
42 | EE | 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) |
41 | EE | Eamonn J. Keogh: Guest Editorial. Machine Learning 58(2-3): 103-105 (2005) |
2004 | ||
40 | EE | Eamonn J. Keogh, Jessica Lin, Stefano Lonardi, Bill Yuan-chi Chiu: We Have Seen the Future, and It Is Symbolic. ACSW Frontiers 2004: 83 |
39 | EE | Jessica Lin, Michail Vlachos, Eamonn J. Keogh, Dimitrios Gunopulos: Iterative Incremental Clustering of Time Series. EDBT 2004: 106-122 |
38 | EE | Themistoklis Palpanas, Michail Vlachos, Eamonn J. Keogh, Dimitrios Gunopulos, Wagner Truppel: Online Amnesic Approximation of Streaming Time Series. ICDE 2004: 338-349 |
37 | EE | Eamonn J. Keogh, Stefano Lonardi, Chotirat (Ann) Ratanamahatana: Towards parameter-free data mining. KDD 2004: 206-215 |
36 | EE | Jessica Lin, Eamonn J. Keogh, Stefano Lonardi, Jeffrey P. Lankford, Donna M. Nystrom: Visually mining and monitoring massive time series. KDD 2004: 460-469 |
35 | EE | Chotirat (Ann) Ratanamahatana, Eamonn J. Keogh: Making Time-Series Classification More Accurate Using Learned Constraints. SDM 2004 |
34 | EE | 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 |
33 | EE | Eamonn J. Keogh, Themis Palpanas, Victor B. Zordan, Dimitrios Gunopulos, Marc Cardle: Indexing Large Human-Motion Databases. VLDB 2004: 780-791 |
32 | EE | Jiyuan An, Yi-Ping Phoebe Chen, Eamonn J. Keogh: A Grid-Based Index Method for Time Warping Distance. WAIM 2004: 65-75 |
2003 | ||
31 | EE | 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 |
30 | EE | Jessica Lin, Eamonn J. Keogh, Wagner Truppel: Clustering of streaming time series is meaningless. DMKD 2003: 56-65 |
29 | 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 | |
28 | EE | Eamonn J. Keogh, Jessica Lin, Wagner Truppel: Clustering of Time Series Subsequences is Meaningless: Implications for Previous and Future Research. ICDM 2003: 115-122 |
27 | EE | Jiyuan An, Hanxiong Chen, Kazutaka Furuse, Nobuo Ohbo, Eamonn J. Keogh: Grid-Based Indexing for Large Time Series Databases. IDEAL 2003: 614-621 |
26 | EE | Michail Vlachos, Marios Hadjieleftheriou, Dimitrios Gunopulos, Eamonn J. Keogh: Indexing multi-dimensional time-series with support for multiple distance measures. KDD 2003: 216-225 |
25 | EE | Bill Yuan-chi Chiu, Eamonn J. Keogh, Stefano Lonardi: Probabilistic discovery of time series motifs. KDD 2003: 493-498 |
24 | EE | Eamonn J. Keogh: Efficiently Finding Arbitrarily Scaled Patterns in Massive Time Series Databases. PKDD 2003: 253-265 |
23 | Eamonn J. Keogh: A Gentle Introduction to Machine Learning and Data Mining for the Database Community. SBBD 2003: 2 | |
22 | EE | 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) |
2002 | ||
21 | EE | Eamonn J. Keogh, Harry Hochheiser, Ben Shneiderman: An Augmented Visual Query Mechanism for Finding Patterns in Time Series Data. FQAS 2002: 240-250 |
20 | EE | Pranav Patel, Eamonn J. Keogh, Jessica Lin, Stefano Lonardi: Mining Motifs in Massive Time Series Databases. ICDM 2002: 370-377 |
19 | EE | Eamonn J. Keogh, Shruti Kasetty: On the need for time series data mining benchmarks: a survey and empirical demonstration. KDD 2002: 102-111 |
18 | EE | 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 |
17 | Eamonn J. Keogh: Indexing and Mining Time Series. SBBD 2002: 9 | |
16 | EE | Selina Chu, Eamonn J. Keogh, David Hart, Michael J. Pazzani: Iterative Deepening Dynamic Time Warping for Time Series. SDM 2002 |
15 | EE | Eamonn J. Keogh: Exact Indexing of Dynamic Time Warping. VLDB 2002: 406-417 |
14 | EE | 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) |
13 | EE | Eamonn J. Keogh, Michael J. Pazzani: Learning the Structure of Augmented Bayesian Classifiers. International Journal on Artificial Intelligence Tools 11(4): 587-601 (2002) |
2001 | ||
12 | EE | Eamonn J. Keogh, Selina Chu, David Hart, Michael J. Pazzani: An Online Algorithm for Segmenting Time Series. ICDM 2001: 289-296 |
11 | EE | Eamonn J. Keogh, Selina Chu, Michael J. Pazzani: Ensemble-index: a new approach to indexing large databases. KDD 2001: 117-125 |
10 | EE | 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 |
9 | EE | 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) |
2000 | ||
8 | EE | Eamonn J. Keogh, Michael J. Pazzani: Scaling up dynamic time warping for datamining applications. KDD 2000: 285-289 |
7 | Eamonn J. Keogh, Michael J. Pazzani: A Simple Dimensionality Reduction Technique for Fast Similarity Search in Large Time Series Databases. PAKDD 2000: 122-133 | |
1999 | ||
6 | Eamonn J. Keogh, Michael J. Pazzani: Scaling up Dynamic Time Warping to Massive Dataset. PKDD 1999: 1-11 | |
5 | EE | Eamonn J. Keogh, Michael J. Pazzani: Relevance Feedback Retrieval of Time Series Data. SIGIR 1999: 183-190 |
4 | EE | Eamonn J. Keogh, Michael J. Pazzani: An Indexing Scheme for Fast Similarity Search in Large Time Series Databases. SSDBM 1999: 56-67 |
1998 | ||
3 | 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 | ||
2 | EE | Eamonn J. Keogh: Fast Similarity Search in the Presence of Longitudinal Scaling in Time Series Databases. ICTAI 1997: 578-584 |
1 | Eamonn J. Keogh, Padhraic Smyth: A Probabilistic Approach to Fast Pattern Matching in Time Series Databases. KDD 1997: 24-30 |