The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Iterative Deepening Dynamic Time Warping for Time Series
Abstract
1 Introduction Time series are a ubiquitous form of data occurring in virtually every scientific discipline and business application. There has been much recent work on adapting data mining algorithms to time series databases. For example, Das et al. attempt to show how association rules can be learned from time series [7]. Debregeas and Hebrail [8] demonstrate a technique for scaling up time series clustering algorithms to massive datasets. Keogh and Pazzani introduced a new, scalable time series classification algorithm [16]. Almost all algorithms that operate on time series data need to compute the similarity between them. Euclidean distance, or some extension or modification thereof, is typically used. However as we will demonstrate in Section 2.1, Euclidean distance can be an extremely brittle distance measure.
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Cites
Fundamentals of speech recognition
Rabiner, L. R., & Juang, B.-H. F. (1993) · Cited by 7,680
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Sakoe, H., & Chiba, S. (1978) · IEEE Transactions on Acoustics Speech and Signal Processing · doi:10.1109/tassp.1978.1163055 · Cited by 6,727
(1994) · Knowledge Discovery and Data Mining · Cited by 3,137
Fast discovery of association rules
Agrawal, R., Mannila, H., Srikant, R., Toivonen, H. T. T., & Verkamo, A. I. (1996) · Knowledge Discovery and Data Mining · Cited by 2,376
Fast subsequence matching in time-series databases
Faloutsos, C., Ranganathan, M., & Manolopoulos, Y. (1994) · Proceedings of the 1994 ACM SIGMOD international conference on Management of data · doi:10.1145/191839.191925 · Cited by 1,709
Dimensionality Reduction for Fast Similarity Search in Large Time Series Databases
Keogh, E., Chakrabarti, K., Pazzani, M., & Mehrotra, S. (2001) · Knowledge and Information Systems · doi:10.1007/pl00011669 · Cited by 1,573
Self-citationScaling up dynamic time warping for datamining applications
Keogh, E. J., & Pazzani, M. J. (2000) · Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/347090.347153 · Cited by 820
Self-citationFast Time Sequence Indexing for Arbitrary Lp Norms
Yi, B.-K., & Faloutsos, C. (2018) · doi:10.1184/r1/6605618 · Cited by 650
Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases
Agrawal, R., Lin, K.-I., Sawhney, H., & Shim, K. (1995) · Cited by 649
Rule discovery from time series
Das, G., Lin, K.-I., Mannila, H., Renganathan, G., & Smyth, P. (1998) · Cited by 637
Cited by
Time-series clustering – A decade review
Aghabozorgi, S., Seyed Shirkhorshidi, A., & Ying Wah, T. (2015) · Information Systems · doi:10.1016/j.is.2015.04.007 · Cited by 1,826
Exact Indexing of Dynamic Time Warping
Keogh, E. (2002) · VLDB '02: Proceedings of the 28th International Conference on Very Large Databases · doi:10.1016/b978-155860869-6/50043-3 · Cited by 532
Self-citationExposure: A Passive DNS Analysis Service to Detect and Report Malicious Domains
Bilge, L., Sen, S., Balzarotti, D., Kirda, E., & Kruegel, C. (2014) · ACM Transactions on Information and System Security · doi:10.1145/2584679 · Cited by 317
Aziz, H. M. A., & Ukkusuri, S. V. (2018) · International Journal of Sustainable Transportation · doi:10.1080/15568318.2017.1346732 · Cited by 15
Metadata
- Authors
- Chu, S., Keogh, E., Hart, D., & Pazzani, M.
- Venue
- Proceedings of the 2002 SIAM International Conference on Data Mining
- Published
- 2002-04-11
- Pages
- 195-212
- OpenAlex
- W69342273
- Cited by
- 268
- References
- 24
- FWCI
- 8.81
- Type
- conference paper
- Language
- en
- Open access
- closed
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