Eamonn Keogh

Eamonn Keogh is a person.

• See: Dimensionality Reduction, Time-Series Motif Discovery, Instance-based Learning.

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References

• Professional Homepage: http://www.cs.ucr.edu/~eamonn/
• DBLP Page: http://www.informatik.uni-trier.de/~ley/db/indices/a-tree/k/Keogh:Eamonn_J=.html
• Google Scholar Author Page: http://scholar.google.com/citations?user=slVcOQIAAAAJ

2011

• (Keogh, 2011a) ⇒ Eamonn Keogh. (2011). “Instance-Based Learning.” In: (Sammut & Webb, 2011) p.549

2006

• (Wei & Keogh, 2006) ⇒ Li Wei, and Eamonn Keogh. (2006). “Semi-supervised Time Series Classification.” In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge discovery and data mining.
• (Ueno et al., 2006) ⇒ Ken Ueno, Xiaopeng Xi, Eamonn Keogh, and Dah-Jye Lee. (2006). “Anytime Classification Using the Nearest Neighbor Algorithm with Applications to Stream Mining.” In: Proceedings of the Sixth International Conference on Data Mining. doi:10.1109/ICDM.2006.21

2005

• (Keogh & Ratanamahatana, 2005) ⇒ Eamonn Keogh, and Chotirat Ann Ratanamahatana. (2005). “Exact Indexing of Dynamic Time Warping.” In: Journal of Knowledge and Information Systems, 7(3). doi:10.1007/s10115-004-0154-9.
  • See: Dynamic Time Warping.

2003

• (Lin et al., 2003) ⇒ Jessica Lin, Eamonn Keogh, Stefano Lonardi, and Bill Chiu. (2003). “A Symbolic Representation of Time Series, with Implications for Streaming Algorithms.” In: Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery. doi:10.1145/882082.882086
• (Chiu et al., 2003) ⇒ Bill Chiu, Eamonn Keogh, and Stefano Lonardi. (2003). “Probabilistic Discovery of Time Series Motifs.” In: Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2003).

2002

• (Keogh & Kasetty, 2002) ⇒ Eamonn Keogh, and Shruti Kasetty. (2002). “On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration.” In: Proceedings of the eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. doi:10.1145/775047.775062
• (Chakrabarti et al., 2002) ⇒ Kaushik Chakrabarti, Eamonn Keogh, Sharad Mehrotra, and Michael Pazzani. (2002). “Locally adaptive dimensionality reduction for indexing large time series databases.” In: ACM Transactions on Database Systems (TODS) 27(2). doi:10.1145/568518.568520
• (Patel et al., 2002) ⇒ Pranav Patel, Eamonn Keogh, Jessica Lin, and Stefano Lonardi. (2002). “Mining Motifs in Massive Time Series Databases.” In: Proceedings of ICDM Conference (ICDM 2002). doi:10.1109/ICDM.2002.1183925

2001

• (Keogh et al., 2001a) ⇒ Eamonn Keogh, Kaushik Chakrabarti, Michael J. Pazzani, and Sharad Mehrotra, (2001). “Dimensionality reduction for fast similarity search in large time series databases.” In: Journal of Knowledge and Information Systems, 3, 3.
• (Keogh et al., 2001b) ⇒ Eamonn Keogh, S. Chu, and Michael J. Pazzani. (2001). “Ensemble-index: A new approach to indexing large databases.” In: Proceedings of the 7th ACM SIGKDD (KDD-2001).
• (Keogh et al., 2001c) ⇒ Eamonn Keogh, Kaushik Chakrabarti, Sharad Mehrotra, and Michael J. Pazzani. (2001). “Locally adaptive dimensionality reduction for indexing large time series databases.” In: Proceedings of the ACM SIGMOD Conference (SIGMOD 2001).

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