E. Keogh, S. Kasetty. On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration. In Prof. 8th ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining. Edmonton Canada, 2002. pp 102-111. Home/Search Document Details and Download
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....technique for indexing DTW. We will compare our DTW indexing technique with the best existing DTW indexing method [14] There is an increasing awareness to use a benchmark approach in time series database experiments to guard against implementation bias and data bias. In the spirit of the work [16, 14], wetooksuch an approach to conduct our experiments. Toavoid data bias, we conducted our experiments on a wide range of time series datasets [13] that cover disciplines including nance, medicine, industry, astronomy and music. We also measured the results in an implementation free fashion to ....
E. J. Keogh and S. Kasetty. On the need for time series data mining benchmarks: A survey and empirical demonstration. In the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,July 23 - 26, 2002.
Online Amnesic Approximation of Streaming Time Series - Palpanas, Vlachos, Keogh, .. (2004) (4 citations) Self-citation (Keogh) (Correct)
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Eamonn J. Keogh and Shruti Kasetty. On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration. In International Conference on Knowledge Discovery and Data Mining, pages 102--111, Edmonton, Canada, July 2002.
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Keogh, E. & Kasetty, S. (2002). On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration. In proceedings of the 8 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. July 23 - 26, 2002. Edmonton, Alberta, Canada. pp 102-111.
Iterative Incremental Clustering of Time Series - Jessica Lin Michail (2004) (2 citations) Self-citation (Keogh) (Correct)
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Keogh, E. & Kasetty, S. (2002). On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration. In proceedings of the 8 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. July 23 - 26, 2002. Edmonton, Alberta, Canada. pp 102-111.
Online Amnesic Approximation of Streaming Time Series - Palpanas, Vlachos, Keogh, .. (2004) (4 citations) Self-citation (Keogh) (Correct)
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Eamonn J. Keogh and Shruti Kasetty. On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration. In International Conference on Knowledge Discovery and Data Mining, pages 102--111, Edmonton, Canada, July 2002.
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....of the Minimum Bounding Envelope only on the query suggests that user queries are not confined to a predefined and rigid matching window #. The user can pose queries of variable warping in time. In some datasets, there is no need to perform warping, since the Euclidean distance performs acceptably [11]. In other datasets, by using the Euclidean distance we can find quickly some very close matches, while using warping we can distinguish more flexible similarities. So, we can start by using a query with # = 0 (no bounding envelope) and increase it progressively in order to find more flexible ....
E. Keogh and S. Kasetty. On the need for time series data mining benchmarks: A survey and empirical demonstration. In Proc. of SIGKDD, 2002.
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E. Keogh, S. Kasetty. On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration. In Prof. 8th ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining. Edmonton Canada, 2002. pp 102-111.
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E. Keogh and S. Kasetty. 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, pages 102--111, Edmonton, Alberta, Canada, July 2002.
Normalization of Life Science Data for Shape-based Similarity.. - Goldin (Correct)
No context found.
E. Keogh, S. Kasetty. On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration. In Prof. 8th ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining. Edmonton Canada, 2002. pp 102-111.
Flexible and Efficient Similarity Querying for Time-series.. - Goldin, Millstein, Kutlu (2003) (Correct)
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E. Keogh, S. Kasetty. On the need for time series data mining benchmarks: a survey and empirical demonstration. Proc. of 8th ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining, pp. 102-111, 2002.
Online Adaption in Learning Classifier Systems: Stream.. - Abbass, Bacardit, Butz, .. (2004) (Correct)
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E. Keogh and S. Kasetty, "On the need for time series data mining benchmarks: a survey and empirical demonstration," in SIGKDD '02, 2002.
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E. Keogh and S. Kasetty, On the need for time series data mining benchmarks: A survey and empirical demonstration, Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining., 2002.
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