Data mining, Artificial intelligence, Cluster analysis, Search engine indexing and Dynamic time warping are his primary areas of study. His work deals with themes such as Set, Automatic summarization and Time series, which intersect with Data mining. His biological study spans a wide range of topics, including Machine learning, Time series classification, Search algorithm and Pattern recognition.
Eamonn Keogh has researched Cluster analysis in several fields, including Association rule learning and Anomaly detection. His work carried out in the field of Search engine indexing brings together such families of science as Database, Triangle inequality, Index, Distance measures and Nearest neighbor search. His Dynamic time warping study combines topics in areas such as Algorithm, Similarity measure, Image warping and Euclidean distance.
Eamonn Keogh spends much of his time researching Data mining, Artificial intelligence, Cluster analysis, Machine learning and Dynamic time warping. The various areas that Eamonn Keogh examines in his Data mining study include Set, Search engine indexing, Distance measures and Time series. His Search engine indexing research is multidisciplinary, incorporating elements of Speedup, Index, Database and Dimensionality reduction.
He has included themes like Domain, Computer vision and Pattern recognition in his Artificial intelligence study. He usually deals with Cluster analysis and limits it to topics linked to Anomaly detection and Theoretical computer science. The concepts of his Dynamic time warping study are interwoven with issues in Algorithm, Nearest neighbor search, Image warping and Euclidean distance.
Eamonn Keogh focuses on Artificial intelligence, Theoretical computer science, Cluster analysis, Dynamic time warping and Machine learning. His Artificial intelligence research integrates issues from Domain and Pattern recognition. He focuses mostly in the field of Cluster analysis, narrowing it down to topics relating to Visualization and, in certain cases, Domain knowledge.
The Dynamic time warping study combines topics in areas such as Nearest neighbor search, Data mining, Image warping and Time series. His work in Data mining tackles topics such as Distance measures which are related to areas like Euclidean distance. In Machine learning, Eamonn Keogh works on issues like Set, which are connected to Data structure.
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Exact indexing of dynamic time warping
Eamonn Keogh;Chotirat Ann Ratanamahatana.
Knowledge and Information Systems (2005)
A symbolic representation of time series, with implications for streaming algorithms
Jessica Lin;Eamonn Keogh;Stefano Lonardi;Bill Chiu.
international conference on management of data (2003)
Exact indexing of dynamic time warping
very large data bases (2002)
Dimensionality reduction for fast similarity search in large time series databases
Eamonn J. Keogh;Kaushik Chakrabarti;Michael J. Pazzani;Sharad Mehrotra.
Knowledge and Information Systems (2001)
On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration
Eamonn Keogh;Shruti Kasetty.
Data Mining and Knowledge Discovery (2003)
Querying and mining of time series data: experimental comparison of representations and distance measures
Hui Ding;Goce Trajcevski;Peter Scheuermann;Xiaoyue Wang.
very large data bases (2008)
Experiencing SAX: a novel symbolic representation of time series
Jessica Lin;Eamonn Keogh;Li Wei;Stefano Lonardi.
Data Mining and Knowledge Discovery (2007)
An online algorithm for segmenting time series
E. Keogh;S. Chu;D. Hart;M. Pazzani.
international conference on data mining (2001)
Derivative Dynamic Time Warping.
Eamonn J. Keogh;Michael J. Pazzani.
siam international conference on data mining (2001)
Locally adaptive dimensionality reduction for indexing large time series databases
Eamonn Keogh;Kaushik Chakrabarti;Michael Pazzani;Sharad Mehrotra.
international conference on management of data (2001)
Profile was last updated on December 6th, 2021.
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