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Scaling up dynamic time warping for datamining applications
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Source International Conference on Knowledge Discovery and Data Mining archive
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining table of contents
Boston, Massachusetts, United States
Pages: 285 - 289  
Year of Publication: 2000
ISBN:1-58113-233-6
Authors
Eamonn J. Keogh  Department of Information and Computer Science, University of California, Irvine, California
Michael J. Pazzani  Department of Information and Computer Science, University of California, Irvine, California
Sponsors
SIGKDD: ACM Special Interest Group on Knowledge Discovery in Data
AAAI : Am Assoc for Artifical Intelligence
SIGART: ACM Special Interest Group on Artificial Intelligence
SIGMOD: ACM Special Interest Group on Management of Data
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 21,   Downloads (12 Months): 203,   Citation Count: 29
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REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Bay, S. (1999). UCI Repository of Kdd databases Department of Information and Computer Science
 
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Berndt, D. & Clifford, J. (1994) Using dynamic time warping to find patterns in time series. AAAI-94 Workshop on Knowledge Discovery in Databases. Seattle, Washington.
 
4
Caiani, E.G., Porta, A., Baselli, G., Turiel, M., Muzzupappa, S., Pieruzzi, F., Crema, C., Malliani, A. & Cerutti, S. (1998) Warped-average template technique to track on a cycle-by-cycle basis the cardiac filling phases on left ventricular volume. IEEE Computers in Cardiology. Vol. 25 Cat.
 
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Das, G., Lin, K., Mannila, H., Renganathan, G. & Smyth, P. (1998). Rule discovery form time series. Proc. of the 4 th International Conference of Knowledge Discovery and Data Mining. pp 16-22, AAAI Press.
 
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Debregeas, A. & Hebrail, G. (1998). Interactive interpretation of Kohonen maps applied to curves. Proc. of the 4 th International Conference of Knowledge Discovery and Data Mining. pp 179-183, AAAI Press.
 
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Derriere, S. (1998) D.E.N.I.S strip 3792: {http://cdsweb.ustrasbg.fr/DENIS/qual_gif/cpl3792.dat}
8
 
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Gavrila, D. M. & Davis,L. S.(1995). Towards 3-d modelbased tracking and recognition of human movement: a multiview approach. In International Workshop on Automatic Face- and Gesture-Recognition. IEEE Computer Society.
 
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Gollmer, K., & Posten, C. (1995) Detection of distorted pattern using dynamic time warping algorithm and application for supervision of bioprocesses. On-Line Fault Detection and Supervision in Chemical Process Industries.
 
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Keogh, E., & Pazzani, M. (1998). An enhanced representation of time series which allows fast and accurate classification, clustering and relevance feedback. Proc. of the 4 th International Conference of Knowledge Discovery and Data Mining. pp 239-241, AAAI Press.
 
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Keogh, E., Smyth, P. (1997). A probabilistic approach to fast pattern matching in time series databases. Proc. of the 3 rd International Conference of Knowledge Discovery and Data Mining. pp 24-20, AAAI Press.
 
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Kruskall, J. B. & Liberman, M. (1983). The symmetric time warping algorithm: From continuous to discrete. In Time Warps, String Edits and Macromolecules. Addison-Wesley.
 
17
 
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Sakoe, H. & Chiba, S. (1978) Dynamic programming algorithm optimization for spoken word recognition. IEEE Trans. Acoustics, Speech, and Signal Proc., Vol. ASSP-26.
 
21
Schmill, M., Oates, T. & Cohen, P. (1999). Learned models for continuous planning. In Seventh International Workshop on Artificial Intelligence and Statistics.
 
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CITED BY  29

Collaborative Colleagues:
Eamonn J. Keogh: colleagues
Michael J. Pazzani: colleagues