H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 96 Citations 50,909 269 World Ranking 188 National Ranking 109

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Algorithm

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.

His most cited work include:

  • A symbolic representation of time series, with implications for streaming algorithms (1407 citations)
  • Exact indexing of dynamic time warping (1342 citations)
  • Dimensionality reduction for fast similarity search in large time series databases (1196 citations)

What are the main themes of his work throughout his whole career to date?

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.

He most often published in these fields:

  • Data mining (41.19%)
  • Artificial intelligence (34.59%)
  • Cluster analysis (27.04%)

What were the highlights of his more recent work (between 2016-2021)?

  • Artificial intelligence (34.59%)
  • Theoretical computer science (10.06%)
  • Cluster analysis (27.04%)

In recent papers he was focusing on the following fields of study:

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.

Between 2016 and 2021, his most popular works were:

  • The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances (475 citations)
  • Generalizing DTW to the multi-dimensional case requires an adaptive approach (93 citations)
  • The UCR time series archive (80 citations)

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Top Publications

Exact indexing of dynamic time warping

Eamonn Keogh;Chotirat Ann Ratanamahatana.
Knowledge and Information Systems (2005)

2661 Citations

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)

2072 Citations

Exact indexing of dynamic time warping

Eamonn Keogh.
very large data bases (2002)

1772 Citations

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)

1700 Citations

On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration

Eamonn Keogh;Shruti Kasetty.
Data Mining and Knowledge Discovery (2003)

1500 Citations

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)

1379 Citations

Experiencing SAX: a novel symbolic representation of time series

Jessica Lin;Eamonn Keogh;Li Wei;Stefano Lonardi.
Data Mining and Knowledge Discovery (2007)

1341 Citations

An online algorithm for segmenting time series

E. Keogh;S. Chu;D. Hart;M. Pazzani.
international conference on data mining (2001)

1300 Citations

Derivative Dynamic Time Warping.

Eamonn J. Keogh;Michael J. Pazzani.
siam international conference on data mining (2001)

1144 Citations

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)

1079 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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