The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
SEGMENTING TIME SERIES: A SURVEY AND NOVEL APPROACH
Abstract
In recent years, there has been an explosion of interest in mining time series databases. As with most computer science problems, representation of the data is the key to efficient and effective solutions. One of the most commonly used representations is piecewise linear approximation. This representation has been used by various researchers to support clustering, classification, indexing and association rule mining of time series data. A variety of algorithms have been proposed to obtain this representation, with several algorithms having been independently rediscovered several times. In this paper, we undertake the first extensive review and empirical comparison of all proposed techniques. We show that all these algorithms have fatal flaws from a data mining perspective. We introduce a novel algorithm that we empirically show to be superior to all others in the literature.
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(1973) · CERN Document Server (European Organization for Nuclear Research) · Cited by 12,895
DOUGLAS, D. H., & PEUCKER, T. K. (1973) · Cartographica · doi:10.3138/fm57-6770-u75u-7727 · Cited by 4,229
Efficient similarity search in sequence databases
Agrawal, R., Faloutsos, C., & Swami, A. (1993) · Lecture Notes in Computer Science · doi:10.1007/3-540-57301-1_5 · Cited by 1,979
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-citationAn iterative procedure for the polygonal approximation of plane curves
Ramer, U. (1972) · Computer Graphics and Image Processing · doi:10.1016/s0146-664x(72)80017-0 · Cited by 1,372
Efficient time series matching by wavelets
Chan, K.-P., & Fu, A. W.-C. (1999) · Proceedings 15th International Conference on Data Engineering (Cat. No.99CB36337) · doi:10.1109/icde.1999.754915 · Cited by 1,031
Rule discovery from time series
Das, G., Lin, K.-I., Mannila, H., Renganathan, G., & Smyth, P. (1998) · Cited by 637
Keogh, E., & Pazzani, M. J. (1998) · Cited by 496
Self-citationSurvey of Polygonal Surface Simplification Algorithms
Heckbert, P. S., & Garland, M. (1997) · Cited by 486
Landmarks: a new model for similarity-based pattern querying in time series databases
Perng, C.-S., Wang, H., Zhang, S. R., & Parker, D. S. (2002) · Proceedings of 16th International Conference on Data Engineering (Cat. No.00CB37073) · doi:10.1109/icde.2000.839385 · Cited by 297
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A survey on ambient-assisted living tools for older adults
Rashidi, P., & Mihailidis, A. (2013) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2012.2234129 · Cited by 1,128
Kim, S.-J., Koh, K., Boyd, S., & Gorinevsky, D. (2009) · SIAM Review · doi:10.1137/070690274 · Cited by 615
Discrete Wavelet transform-based time series analysis and mining
Chaovalit, P., Gangopadhyay, A., Karabatis, G., & Chen, Z. (2011) · ACM Computing Surveys · doi:10.1145/1883612.1883613 · Cited by 218
Mobile Data Science and Intelligent Apps: Concepts, AI-Based Modeling and Research Directions
Sarker, I. H., Hoque, M. M., Uddin, M. K., & Alsanoosy, T. (2021) · Mobile Networks and Applications · doi:10.1007/s11036-020-01650-z · Cited by 207
Metadata
- Authors
- Keogh, E., Chu, S., Hart, D., & Pazzani, M.
- Venue
- Series in Machine Perception and Artificial Intelligence
- Publisher
- WORLD SCIENTIFIC
- Published
- 2004-06
- Pages
- 1-21
- ISSN
- 1793-0839 (ISSN-L)
- OpenAlex
- W2483430316
- Cited by
- 600
- References
- 28
- FWCI
- 4.86
- Type
- book chapter
- Language
- en
- Open access
- closed
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