Eamonn J. Keogh - researchr alias

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2011

An efficient and effective similarity measure to enable data mining of petroglyphsQiang Zhu 0002, Xiaoyue Wang, Eamonn J. Keogh, Sang-Hee Lee. datamine, 23(1):91-127, 2011. [doi]

A Complexity-Invariant Distance Measure for Time SeriesGustavo E. A. P. A. Batista, Xiaoyue Wang, Eamonn J. Keogh. sdm 2011: 699-710 [doi]

SIGKDD demo: sensors and software to allow computational entomology, an emerging application of data miningGustavo E. A. P. A. Batista, Eamonn J. Keogh, Agenor Mafra-Neto, Edgar Rowton. kdd 2011: 761-764 [doi]

Logical-shapelets: an expressive primitive for time series classificationAbdullah Mueen, Eamonn J. Keogh, Neal Young. kdd 2011: 1154-1162 [doi]

A disk-aware algorithm for time series motif discoveryAbdullah Mueen, Eamonn J. Keogh, Qiang Zhu 0002, Sydney Cash, M. Brandon Westover, Nima Bigdely Shamlo. datamine, 22(1-2):73-105, 2011. [doi]

Time series shapelets: a novel technique that allows accurate, interpretable and fast classificationLexiang Ye, Eamonn J. Keogh. datamine, 22(1-2):149-182, 2011. [doi]

2010

Using CAPTCHAs to Index Cultural ArtifactsQiang Zhu 0002, Eamonn J. Keogh. ida 2010: 245-257 [doi]

Mother Fugger: Mining Historical Manuscripts with Local Color PatchesQiang Zhu 0002, Eamonn J. Keogh. icdm 2010: 699-708 [doi]

Classification of Live Moths Combining Texture, Color and Shape PrimitivesGustavo E. A. P. A. Batista, Bilson J. L. Campana, Eamonn J. Keogh. icmla 2010: 903-906 [doi]

iSAX 2.0: Indexing and Mining One Billion Time SeriesAlessandro Camerra, Themis Palpanas, Jin Shieh, Eamonn J. Keogh. icdm 2010: 58-67 [doi]

A compression-based distance measure for textureBilson J. L. Campana, Eamonn J. Keogh. sadm, 3(6):381-398, 2010. [doi]

A Compression Based Distance Measure for TextureBilson J. L. Campana, Eamonn J. Keogh. sdm 2010: 850-861 [doi]

Online discovery and maintenance of time series motifsAbdullah Mueen, Eamonn J. Keogh. kdd 2010: 1089-1098 [doi]

Data Editing Techniques to Allow the Application of Distance-Based Outlier Detection to StreamsVit Niennattrakul, Eamonn J. Keogh, Chotirat Ann Ratanamahatana. icdm 2010: 947-952 [doi]

Accelerating Dynamic Time Warping Subsequence Search with GPUs and FPGAsDoruk Sart, Abdullah Mueen, Walid A. Najjar, Eamonn J. Keogh, Vit Niennattrakul. icdm 2010: 1001-1006 [doi]

Polishing the Right Apple: Anytime Classification Also Benefits Data Streams with Constant Arrival TimesJin Shieh, Eamonn J. Keogh. icdm 2010: 461-470 [doi]

Annotating Historical Archives of ImagesXiaoyue Wang, Lexiang Ye, Eamonn J. Keogh, Christian R. Shelton. ijdls, 1(2):59-80, 2010. [doi]

A brief survey on sequence classificationZhengzheng Xing, Jian Pei, Eamonn J. Keogh. sigkdd, 12(1):40-48, 2010. [doi]

2009

Supporting exact indexing of arbitrarily rotated shapes and periodic time series under Euclidean and warping distance measuresEamonn J. Keogh, Li Wei, Xiaopeng Xi, Michail Vlachos, Sang-Hee Lee, Pavlos Protopapas. vldb, 18(3):611-630, 2009. [doi]

Exact Discovery of Time Series MotifsAbdullah Mueen, Eamonn J. Keogh, Qiang Zhu, Sydney Cash, M. Brandon Westover. sdm 2009: 473-484 [doi]

Finding Time Series Motifs in Disk-Resident DataAbdullah Mueen, Eamonn J. Keogh, Nima Bigdely Shamlo. icdm 2009: 367-376 [doi]

::::i::::SAX: disk-aware mining and indexing of massive time series datasetsJin Shieh, Eamonn J. Keogh. datamine, 19(1):24-57, 2009. [doi]

Finding centuries-old hyperlinks with a novel semi-supervised learning techniqueXiaoyue Wang, Eamonn J. Keogh. JCDL 2009: 451-452 [doi]

Augmenting Historical Manuscripts with Automatic HyperlinksXiaoyue Wang, Eamonn J. Keogh. ism 2009: 571-576 [doi]

Autocannibalistic and Anyspace Indexing Algorithms with Application to Sensor Data MiningLexiang Ye, Xiaoyue Wang, Eamonn J. Keogh, Agenor Mafra-Neto. sdm 2009: 85-96 [doi]

Time series shapelets: a new primitive for data miningLexiang Ye, Eamonn J. Keogh. kdd 2009: 947-956 [doi]

Augmenting the generalized hough transform to enable the mining of petroglyphsQiang Zhu, Xiaoyue Wang, Eamonn J. Keogh, Sang-Hee Lee. kdd 2009: 1057-1066 [doi]

2008

Querying and mining of time series data: experimental comparison of representations and distance measuresHui Ding, Goce Trajcevski, Peter Scheuermann, Xiaoyue Wang, Eamonn J. Keogh. pvldb, 1(2):1542-1552, 2008. [doi]

Scaling and time warping in time series queryingAda Wai-Chee Fu, Eamonn J. Keogh, Leo Yung Hang Lau, Chotirat (Ann) Ratanamahatana, Raymond Chi-Wing Wong. vldb, 17(4):899-921, 2008. [doi]

Real-Time Classification of Streaming Sensor DataShashwati Kasetty, Candice Stafford, Gregory P. Walker, Xiaoyue Wang, Eamonn J. Keogh. ictai 2008: 149-156 [doi]

Streaming Time Series Summarization Using User-Defined Amnesic FunctionsThemis Palpanas, Michail Vlachos, Eamonn J. Keogh, Dimitrios Gunopulos. tkde, 20(7):992-1006, 2008. [doi]

::::i::::SAX: indexing and mining terabyte sized time seriesJin Shieh, Eamonn J. Keogh. kdd 2008: 623-631 [doi]

Annotating historical archives of imagesXiaoyue Wang, Lexiang Ye, Eamonn J. Keogh, Christian R. Shelton. JCDL 2008: 341-350 [doi]

A Clustering Analysis for Target Group Identification by Locality in Motor Insurance IndustryXiaozhe Wang, Eamonn J. Keogh. In Bhanu Prasad, editor, Soft Computing Applications in Business. Volume 230 of Studies in Fuzziness and Soft Computing, pages 113-127, Springer, 2008. [doi]

Efficiently finding unusual shapes in large image databasesLi Wei, Eamonn J. Keogh, Xiaopeng Xi, Melissa Yoder. datamine, 17(3):343-376, 2008. [doi]

Converting non-parametric distance-based classification to anytime algorithmsXiaopeng Xi, Ken Ueno, Eamonn J. Keogh, Dah-Jye Lee. paa, 11(3-4):321-336, 2008. [doi]

Fast Best-Match Shape Searching in Rotation-Invariant Metric SpacesDragomir Yankov, Eamonn J. Keogh, Li Wei, Xiaopeng Xi, Wendy L. Hodges. tmm, 10(2):230-239, 2008. [doi]

Disk aware discord discovery: finding unusual time series in terabyte sized datasetsDragomir Yankov, Eamonn J. Keogh, Umaa Rebbapragada. kais, 17(2):241-262, 2008. [doi]

The Asymmetric Approximate Anytime Join: A New Primitive with Applications to Data MiningLexiang Ye, Xiaoyue Wang, Dragomir Yankov, Eamonn J. Keogh. sdm 2008: 363-374 [doi]

2007

TS2-tree - an efficient similarity based organization for trajectory dataPetko Bakalov, Eamonn J. Keogh, Vassilis J. Tsotras. gis 2007: 58 [doi]

WAT: Finding Top-K Discords in Time Series DatabaseYingyi Bu, Oscar Tat-Wing Leung, Ada Wai-Chee Fu, Eamonn J. Keogh, Jian Pei, Sam Meshkin. sdm 2007: [doi]

Domain-Driven, Actionable Knowledge DiscoveryLongbing 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. expert, 22(4):78-88, 2007. [doi]

Finding the most unusual time series subsequence: algorithms and applicationsEamonn J. Keogh, Jessica Lin, Sang-Hee Lee, Helga Van Herle. kais, 11(1):1-27, 2007. [doi]

Compression-based data mining of sequential dataEamonn J. Keogh, Stefano Lonardi, Chotirat Ann Ratanamahatana, Li Wei, Sang-Hee Lee, John Handley. datamine, 14(1):99-129, 2007. [doi]

Experiencing SAX: a novel symbolic representation of time seriesJessica Lin, Eamonn J. Keogh, Li Wei, Stefano Lonardi. datamine, 15(2):107-144, 2007. [doi]

Visual Exploration of Genomic DataMichail Vlachos, Bahar Taneri, Eamonn J. Keogh, Philip S. Yu. pkdd 2007: 613-620 [doi]

Efficient query filtering for streaming time series with applications to semisupervised learning of time series classifiersLi Wei, Eamonn J. Keogh, Helga Van Herle, Agenor Mafra-Neto, Russell J. Abbott. kais, 11(3):313-344, 2007. [doi]

Finding Motifs in a Database of ShapesXiaopeng Xi, Eamonn J. Keogh, Li Wei, Agenor Mafra-Neto. sdm 2007: [doi]

Detecting time series motifs under uniform scalingDragomir Yankov, Eamonn J. Keogh, Jose Medina, Bill Yuan-chi Chiu, Victor B. Zordan. kdd 2007: 844-853 [doi]

Locally Constrained Support Vector ClusteringDragomir Yankov, Eamonn J. Keogh, Kin Fai Kan. icdm 2007: 715-720 [doi]

Fast Best-Match Shape Searching in Rotation Invariant Metric SpacesDragomir Yankov, Eamonn J. Keogh, Li Wei, Xiaopeng Xi, Wendy L. Hodges. sdm 2007: [doi]

Disk Aware Discord Discovery: Finding Unusual Time Series in Terabyte Sized DatasetsDragomir Yankov, Eamonn J. Keogh, Umaa Rebbapragada. icdm 2007: 381-390 [doi]

2006

Global distance-based segmentation of trajectoriesAris Anagnostopoulos, Michail Vlachos, Marios Hadjieleftheriou, Eamonn J. Keogh, Philip S. Yu. kdd 2006: 34-43 [doi]

A Bit Level Representation for Time Series Data Mining with Shape Based SimilarityAnthony J. Bagnall, Chotirat (Ann) Ratanamahatana, Eamonn J. Keogh, Stefano Lonardi, Gareth J. Janacek. datamine, 13(1):11-40, 2006. [doi]

Finding Time Series Discords Based on Haar TransformAda Wai-Chee Fu, Oscar Tat-Wing Leung, Eamonn J. Keogh, Jessica Lin. adma 2006: 31-41 [doi]

Finding Unusual Medical Time-Series Subsequences: Algorithms and ApplicationsEamonn J. Keogh, Jessica Lin, Ada Wai-Chee Fu, Helga Van Herle. titb, 10(3):429-439, 2006. [doi]

LB_Keogh Supports Exact Indexing of Shapes under Rotation Invariance with Arbitrary Representations and Distance MeasuresEamonn J. Keogh, Li Wei, Xiaopeng Xi, Sang-Hee Lee, Michail Vlachos. VLDB 2006: 882-893 [doi]

A Decade of Progress in Indexing and Mining Large Time Series DatabasesEamonn J. Keogh. VLDB 2006: 1268 [doi]

Intelligent Icons: Integrating Lite-Weight Data Mining and Visualization into GUI Operating SystemsEamonn J. Keogh, Li Wei, Xiaopeng Xi, Stefano Lonardi, Jin Shieh, Scott Sirowy. icdm 2006: 912-916 [doi]

Data mining and information retrieval in time series/multimedia databasesEamonn J. Keogh. mm 2006: 10 [doi]

Group SAX: Extending the Notion of Contrast Sets to Time Series and Multimedia DataJessica Lin, Eamonn J. Keogh. pkdd 2006: 284-296 [doi]

Efficient Discovery of Unusual Patterns in Time SeriesStefano Lonardi, Jessica Lin, Eamonn J. Keogh, Bill Yuan-chi Chiu. ngc, 25(1):61-93, 2006. [doi]

Anytime Classification Using the Nearest Neighbor Algorithm with Applications to Stream MiningKen Ueno, Xiaopeng Xi, Eamonn J. Keogh, Dah-Jye Lee. icdm 2006: 623-632 [doi]

Configurable cache subsetting for fast cache tuningPablo Viana, Ann Gordon-Ross, Eamonn J. Keogh, Edna Barros, Frank Vahid. dac 2006: 695-700 [doi]

Indexing Multidimensional Time-SeriesMichail Vlachos, Marios Hadjieleftheriou, Dimitrios Gunopulos, Eamonn J. Keogh. vldb, 15(1):1-20, 2006. [doi]

SAXually Explicit Images: Finding Unusual ShapesLi Wei, Eamonn J. Keogh, Xiaopeng Xi. icdm 2006: 711-720 [doi]

Semi-supervised time series classificationLi Wei, Eamonn J. Keogh. kdd 2006: 748-753 [doi]

Clustering Workflow Requirements Using Compression Dissimilarity MeasureLi Wei, John Handley, Nathaniel Martin, Tong Sun, Eamonn J. Keogh. icdm 2006: 50-54 [doi]

Fast time series classification using numerosity reductionXiaopeng Xi, Eamonn J. Keogh, Christian R. Shelton, Li Wei, Chotirat Ann Ratanamahatana. icml 2006: 1033-1040 [doi]

Ensembles of Nearest Neighbor ForecastsDragomir Yankov, Dennis DeCoste, Eamonn J. Keogh. ecml 2006: 545-556 [doi]

Manifold Clustering of ShapesDragomir Yankov, Eamonn J. Keogh. icdm 2006: 1167-1171 [doi]

2005

Multimedia Retrieval Using Time Series Representation and Relevance FeedbackChotirat (Ann) Ratanamahatana, Eamonn J. Keogh. ICADL 2005: 400-405 [doi]

Using Relevance Feedback to Learn Both the Distance Measure and the Query in Multimedia DatabasesChotirat (Ann) Ratanamahatana, Eamonn J. Keogh. kes 2005: 16-23 [doi]

A Novel Bit Level Time Series Representation with Implication of Similarity Search and ClusteringChotirat (Ann) Ratanamahatana, Eamonn J. Keogh, Anthony J. Bagnall, Stefano Lonardi. pakdd 2005: 771-777 [doi]

Mining Time Series DataChotirat (Ann) Ratanamahatana, Jessica Lin, Dimitrios Gunopulos, Eamonn J. Keogh, Michail Vlachos, Gautam Das. In Oded Maimon, Lior Rokach, editors, The Data Mining and Knowledge Discovery Handbook. pages 1069-1103, Springer, 2005.

Three Myths about Dynamic Time Warping Data MiningChotirat (Ann) Ratanamahatana, Eamonn J. Keogh. sdm 2005:

Efficient trajectory joins using symbolic representationsPetko Bakalov, Marios Hadjieleftheriou, Eamonn J. Keogh, Vassilis J. Tsotras. MDM 2005: 86-93 [doi]

Scaling and Time Warping in Time Series QueryingAda Wai-Chee Fu, Eamonn J. Keogh, Leo Yung Hang Lau, Chotirat (Ann) Ratanamahatana. VLDB 2005: 649-660 [doi]

Exact indexing of dynamic time warpingEamonn J. Keogh, Chotirat (Ann) Ratanamahatana. kais, 7(3):358-386, 2005. [doi]

HOT SAX: Efficiently Finding the Most Unusual Time Series SubsequenceEamonn J. Keogh, Jessica Lin, Ada Wai-Chee Fu. icdm 2005: 226-233 [doi]

Clustering of time-series subsequences is meaningless: implications for previous and future researchEamonn J. Keogh, Jessica Lin. kais, 8(2):154-177, 2005. [doi]

Guest EditorialEamonn J. Keogh. ml, 58(2-3):103-105, 2005. [doi]

Recent Advances in Mining Time Series DataEamonn J. Keogh. pkdd 2005: 6 [doi]

Visualization and Mining of Temporal DataEamonn J. Keogh. visualization 2005: 126 [doi]

Time-series Bitmaps: a Practical Visualization Tool for Working with Large Time Series DatabasesNitin Kumar, Venkata Nishanth Lolla, Eamonn J. Keogh, Stefano Lonardi, Chotirat (Ann) Ratanamahatana. sdm 2005:

Elastic Partial Matching of Time SeriesLongin Jan Latecki, Vasilis Megalooikonomou, Qiang Wang, Rolf Lakämper, Chotirat (Ann) Ratanamahatana, Eamonn J. Keogh. pkdd 2005: 577-584 [doi]

Partial Elastic Matching of Time SeriesLongin Jan Latecki, Vasileios Megalooikonomou, Qiang Wang, Rolf Lakämper, Chotirat (Ann) Ratanamahatana, Eamonn J. Keogh. icdm 2005: 701-704 [doi]

Approximations to Magic: Finding Unusual Medical Time SeriesJessica Lin, Eamonn J. Keogh, Ada Wai-Chee Fu, Helga Van Herle. cbms 2005: 329-334 [doi]

Visualizing and discovering non-trivial patterns in large time series databasesJessica Lin, Eamonn J. Keogh, Stefano Lonardi. ivs, 4(2):61-82, 2005. [doi]

A MPAA-Based Iterative Clustering Algorithm Augmented by Nearest Neighbors Search for Time-Series Data StreamsJessica Lin, Michail Vlachos, Eamonn J. Keogh, Dimitrios Gunopulos, Jian-Wei Liu, Shou-jian Yu, Jia-Jin Le. pakdd 2005: 333-342 [doi]

Indexing Multi-Dimensional Trajectories for Similarity QueriesMichail Vlachos, Marios Hadjieleftheriou, Eamonn J. Keogh, Dimitrios Gunopulos. In Yannis Manolopoulos, Apostolos Papadopoulos, Michael Vassilakopoulos, editors, Spatial Databases: Technologies, Techniques and Trends. pages 107-128, Idea Group, 2005.

Assumption-Free Anomaly Detection in Time SeriesLi Wei, Nitin Kumar, Venkata Nishanth Lolla, Eamonn J. Keogh, Stefano Lonardi, Chotirat (Ann) Ratanamahatana. ssdbm 2005: 237-240

Atomic Wedgie: Efficient Query Filtering for Streaming Times SeriesLi Wei, Eamonn J. Keogh, Helga Van Herle, Agenor Mafra-Neto. icdm 2005: 490-497 [doi]

A Practical Tool for Visualizing and Data Mining Medical Time SeriesLi Wei, Nitin Kumar, Venkata Nishanth Lolla, Eamonn J. Keogh, Stefano Lonardi, Chotirat (Ann) Ratanamahatana, Helga Van Herle. cbms 2005: 341-346 [doi]

Integrating Lite-Weight but Ubiquitous Data Mining into GUI Operating SystemsLi Wei, Eamonn J. Keogh, Xiaopeng Xi, Stefano Lonardi. jucs, 11(11):1820-1834, 2005. [doi]

Dot Plots for Time Series AnalysisDragomir Yankov, Eamonn J. Keogh, Stefano Lonardi, Ada Wai-Chee Fu. ictai 2005: 159-168 [doi]

2004

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