E
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
More filters
Journal ArticleDOI
Exact indexing of dynamic time warping
TL;DR: This work introduces a novel technique for the exact indexing of Dynamic time warping and proves its vast superiority over all competing approaches in the largest and most comprehensive set of time series indexing experiments ever undertaken.
Proceedings ArticleDOI
A symbolic representation of time series, with implications for streaming algorithms
TL;DR: A new symbolic representation of time series is introduced that is unique in that it allows dimensionality/numerosity reduction, and it also allows distance measures to be defined on the symbolic approach that lower bound corresponding distance measuresdefined on the original series.
Journal ArticleDOI
Dimensionality reduction for fast similarity search in large time series databases
TL;DR: This work introduces a new dimensionality reduction technique which it is called Piecewise Aggregate Approximation (PAA), and theoretically and empirically compare it to the other techniques and demonstrate its superiority.
Journal ArticleDOI
Experiencing SAX: a novel symbolic representation of time series
TL;DR: The utility of the new symbolic representation of time series formed is demonstrated, which allows dimensionality/numerosity reduction, and it also allows distance measures to be defined on the symbolic approach that lower bound corresponding distance measuresdefined on the original series.
Journal ArticleDOI
Querying and mining of time series data: experimental comparison of representations and distance measures
TL;DR: An extensive set of time series experiments are conducted re-implementing 8 different representation methods and 9 similarity measures and their variants and testing their effectiveness on 38 time series data sets from a wide variety of application domains to provide a unified validation of some of the existing achievements.