Identifying distinctive subsequences in multivariate time series by clustering

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Identifying distinctive subsequences in multivariate time series by clustering

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International Conference on Knowledge Discovery and Data Mining archive
Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining table of contents

San Diego, California, United States

Pages: 322 - 326

Year of Publication: 1999

ISBN:1-58113-143-7

Author

Tim Oates

Computer Science Department, LGRC, University of Massachusetts, Box 34610, Amherst, MA

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

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Downloads (6 Weeks): 8, Downloads (12 Months): 60, Citation Count: 14

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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.

	1	Rakesh Agrawal , King-Ip Lin , Harpreet S. Sawhney , Kyuseok Shim, Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases, Proceedings of the 21th International Conference on Very Large Data Bases, p.490-501, September 11-15, 1995
	2	Berndt, D. J., and Clifford,~ J. 1994. Using dynamic time warping to find patterns in time series. In Working Notes of the Knowledge Discovery in Databases Workshop, 359-370.
	3	Thomas T. Cormen , Charles E. Leiserson , Ronald L. Rivest, Introduction to algorithms, MIT Press, Cambridge, MA, 1990
	4	Das, G.; Lin, K.-I.; Mannila, H.; Renganathan, G.; and Smyth, P. 1998. Rule discovery from time series. In Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining, 16-22.
	5	Frederick Jelinek, Statistical methods for speech recognition, MIT Press, Cambridge, MA, 1998
	6	Keogh, E., and Pazzani, M. J. 1998. An enhanced representation of time series which allows fast and accurate classification, clustering and relevance feedback. In Working Notes of the AAAI-98 workshop on Predicting the Future: AI Approaches to Time-Series Analysis, 44-51.
	7	Kruskall, J. B., and Sankoff, D. 1983. An anthology of algorithms and concepts for sequence comparison. In Sankoff, D., and Kruskall, J. B., eds., Time Warps, String Edits and Macromolecules: The Theory and Practice of Sequence Comparison. Addison-Wesley.
	8	Picone, J.W. 1993. Signal modeling techniques in speech recognition. Proceedings of the IEEE 89(9):1215-1247.
	9	Sankoff, D., and Kruskall, J. B. 1983. Time Warps, String Edits and Macromolecules: The Theory and Practice of Sequence Comparison. Addison-Wesley.

CITED BY 14

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	Tim Oates , Matthew D. Schmill , Paul R. Cohen, Identifying qualitatively different outcomes of actions: gaining autonomy through learning, Proceedings of the fourth international conference on Autonomous agents, p.110-111, June 03-07, 2000, Barcelona, Spain
	Marco Ramoni , Paola Sebastiani , Paul Cohen, Bayesian Clustering by Dynamics, Machine Learning, v.47 n.1, p.91-121, April 2002
	Jessica Lin , Eamonn Keogh , Wagner Truppel, Clustering of streaming time series is meaningless, Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery, June 13-13, 2003, San Diego, California
	Kiyoung Yang , Cyrus Shahabi, A PCA-based similarity measure for multivariate time series, Proceedings of the 2nd ACM international workshop on Multimedia databases, November 13-13, 2004, Washington, DC, USA
	John F. Roddick , Myra Spiliopoulou, A Survey of Temporal Knowledge Discovery Paradigms and Methods, IEEE Transactions on Knowledge and Data Engineering, v.14 n.4, p.750-767, July 2002
	Jessica Lin , Eamonn Keogh , Stefano Lonardi , Jeffrey P. Lankford , Donna M. Nystrom, Visually mining and monitoring massive time series, Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, August 22-25, 2004, Seattle, WA, USA
	Jessica Lin , Eamonn Keogh , Stefano Lonardi, Visualizing and discovering non-trivial patterns in large time series databases, Information Visualization, v.4 n.2, p.61-82, July 2005
	Jiong Yang , Wei Wang , Philip S. Yu, Mining Asynchronous Periodic Patterns in Time Series Data, IEEE Transactions on Knowledge and Data Engineering, v.15 n.3, p.613-628, March 2003
	Charu C. Aggarwal, On effective classification of strings with wavelets, Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, July 23-26, 2002, Edmonton, Alberta, Canada
	Jason R. Chen, Making clustering in delay-vector space meaningful, Knowledge and Information Systems, v.11 n.3, p.369-385, April 2007
	Jiong Yang , Wei Wang , Philip S. Yu, Mining Surprising Periodic Patterns, Data Mining and Knowledge Discovery, v.9 n.2, p.189-216, September 2004
	Charu C. Aggarwal , Paul S. Bradley, On the Use of Wavelet Decomposition for String Classification, Data Mining and Knowledge Discovery, v.10 n.2, p.117-139, March 2005
	Chen Yu , Yiwen Zhong , Thomas Smith , Ikhyun Park , Weixia Huang, Visual data mining of multimedia data for social and behavioral studies, Information Visualization, v.8 n.1, p.56-70, January 2009