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    Author information
    First name: Eamonn J.
    Last name: Keogh
    DBLP: k/EamonnJKeogh
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    Below you find the publications which have been written by this author.

    Show item 1 to 25 of 218  
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    Journal article
    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. 2017, Volume 31 (0) 2017
    Journal article
    Mohammad Shokoohi-Yekta, Bing Hu 0001, Hongxia Jin, Jun Wang, Eamonn J. Keogh.
    Generalizing DTW to the multi-dimensional case requires an adaptive approach.
    Data Min. Knowl. Discov. 2017, Volume 31 (0) 2017
    Conference paper
    Eamonn J. Keogh.
    Nearest Neighbor.
    Encyclopedia of Machine Learning and Data Mining 2017 (0) 2017
    Conference paper
    Eamonn J. Keogh.
    Time Series.
    Encyclopedia of Machine Learning and Data Mining 2017 (0) 2017
    Conference paper
    Eamonn J. Keogh, Abdullah Mueen.
    Curse of Dimensionality.
    Encyclopedia of Machine Learning and Data Mining 2017 (0) 2017
    Conference paper
    Eamonn J. Keogh.
    Instance-Based Learning.
    Encyclopedia of Machine Learning and Data Mining 2017 (0) 2017
    Conference paper
    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. 2017, Volume 31 (0) 2017
    Book chapter
    Eamonn J. Keogh.
    Indexing and Mining Time Series Data.
    Encyclopedia of GIS. 2017 (0) 2017
    Conference paper
    Yilin Shen, Yanping Chen 0005, Eamonn J. Keogh, Hongxia Jin.
    Searching Time Series with Invariance to Large Amounts of Uniform Scaling.
    33rd IEEE International Conference on Data Engineering, ICDE 2017, San Diego, CA, USA, April 19-22, 2017 2017 (0) 2017
    Conference paper
    Yifei Ding, Eamonn J. Keogh.
    Query Suggestion to allow Intuitive Interactive Search in Multidimensional Time Series.
    Proceedings of the 29th International Conference on Scientific and Statistical Database Management, Chicago, IL, USA, June 27-29, 2017 2017 (0) 2017
    Conference paper
    Hoang Anh Dau, Eamonn J. Keogh.
    Matrix Profile V: A Generic Technique to Incorporate Domain Knowledge into Motif Discovery.
    Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada, August 13 - 17, 2017 2017 (0) 2017
    Conference paper
    Chin-Chia Michael Yeh, Nickolas Kavantzas, Eamonn J. Keogh.
    Matrix Profile IV: Using Weakly Labeled Time Series to Predict Outcomes.
    PVLDB 2016, Volume 10 (0) 2017
    Journal article
    Jesin Zakaria, Abdullah Mueen, Eamonn J. Keogh, Neal E. Young.
    Accelerating the discovery of unsupervised-shapelets.
    Data Min. Knowl. Discov. 2016, Volume 30 (0) 2016
    Conference paper
    Abdullah Mueen, Eamonn J. Keogh.
    Extracting Optimal Performance from Dynamic Time Warping.
    Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, August 13-17, 2016 2016 (0) 2016
    Conference paper
    Yan Zhu 0014, Eamonn J. Keogh.
    Irrevocable-choice algorithms for sampling from a stream.
    Data Min. Knowl. Discov. 2016, Volume 30 (0) 2016
    Conference paper
    Diego Furtado Silva, Chin-Chia Michael Yeh, Gustavo E. A. P. A. Batista, Eamonn J. Keogh.
    SiMPle: Assessing Music Similarity Using Subsequences Joins.
    Proceedings of the 17th International Society for Music Information Retrieval Conference, ISMIR 2016, New York City, United States, August 7-11, 2016 2016 (0) 2016
    Conference paper
    Liudmila Ulanova, Nurjahan Begum, Mohammad Shokoohi-Yekta, Eamonn J. Keogh.
    Clustering in the Face of Fast Changing Streams.
    Proceedings of the 2016 SIAM International Conference on Data Mining, Miami, Florida, USA, May 5-7, 2016 2016 (0) 2016
    Conference paper
    Hoang Anh Dau, Nurjahan Begum, Eamonn J. Keogh.
    Semi-Supervision Dramatically Improves Time Series Clustering under Dynamic Time Warping.
    Proceedings of the 25th ACM International Conference on Information and Knowledge Management, CIKM 2016, Indianapolis, IN, USA, October 24-28, 2016 2016 (0) 2016
    Conference paper
    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 2016, Volume 0 (0) 2016
    Conference paper
    Diego Furtado Silva, Gustavo E. A. P. A. Batista, Eamonn J. Keogh.
    Prefix and Suffix Invariant Dynamic Time Warping.
    IEEE 16th International Conference on Data Mining, ICDM 2016, December 12-15, 2016, Barcelona, Spain 2016 (0) 2016
    Conference paper
    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.
    IEEE 16th International Conference on Data Mining, ICDM 2016, December 12-15, 2016, Barcelona, Spain 2016 (0) 2016
    Conference paper
    Yan Zhu 0014, Zachary Schall-Zimmerman, Nader Shakibay Senobari, Chin-Chia Michael Yeh, Gareth 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.
    IEEE 16th International Conference on Data Mining, ICDM 2016, December 12-15, 2016, Barcelona, Spain 2016 (0) 2016
    Conference paper
    Chin-Chia Michael Yeh, Yan Zhu 0014, 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.
    IEEE 16th International Conference on Data Mining, ICDM 2016, December 12-15, 2016, Barcelona, Spain 2016 (0) 2016
    Conference paper
    Bing Hu 0001, 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. 2015, Volume 29 (0) 2015
    Conference paper
    Bing Hu 0001, 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. 2015, Volume 18 (0) 2015
    Show item 1 to 25 of 218  

    Your query returned 218 matches in the database.