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Eamonn Keogh
Researcher at University of California, Riverside
Publications - 316
Citations - 44557
Eamonn Keogh is an academic researcher from University of California, Riverside. The author has contributed to research in topics: Cluster analysis & Dynamic time warping. The author has an hindex of 89, co-authored 306 publications receiving 39292 citations. Previous affiliations of Eamonn Keogh include University of California & University of California, Irvine.
Papers
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Proceedings ArticleDOI
Matrix Profile II: Exploiting a Novel Algorithm and GPUs to Break the One Hundred Million Barrier for Time Series Motifs and Joins
Yan Zhu,Zachary Zimmerman,Nader Shakibay Senobari,Chin-Chia Michael Yeh,Gareth J. Funning,Abdullah Mueen,Philip Brisk,Eamonn Keogh +7 more
TL;DR: This work shows that a combination of a novel algorithm and a high-performance GPU allows us to significantly improve the scalability of motif discovery, and demonstrates the scalable of the ideas by finding the full set of exact motifs on a dataset with one hundred million subsequences.
Proceedings Article
Time-series Bitmaps: a Practical Visualization Tool for Working with Large Time Series Databases.
TL;DR: This work introduces a simple parameter-light tool that allows users to efficiently navigate through large collections of time series and can be embedded directly into any standard graphical user interfaces, such as Microsoft Windows, thus making deployment easier.
A Wavelet-Based Anytime Algorithm for K-Means Clustering of Time Series
TL;DR: This work introduces a novel anytime version of k-Means clustering algorithm for time series by leveraging off the multi-resolution property of wavelets and explains, and empirically demonstrates, two surprising and desirable properties of the algorithm.
Book ChapterDOI
Mining Time Series Data
Chotirat Ann Ratanamahatana,Jessica Lin,Dimitrios Gunopulos,Eamonn Keogh,Michail Vlachos,Gautam Das +5 more
TL;DR: This chapter gives a high-level survey of time series Data Mining tasks, with an emphasis on time series representations.
Proceedings ArticleDOI
Online discovery and maintenance of time series motifs
Abdullah Mueen,Eamonn Keogh +1 more
TL;DR: This paper develops the first online motif discovery algorithm which monitors and maintains motifs exactly in real time over the most recent history of a stream and allows useful extensions of the algorithm to deal with arbitrary data rates and discovering multidimensional motifs.