An online algorithm for segmenting time series. Keogh, E., Chu, S., Hart, D., & Pazzani, M. In Proceedings 2001 IEEE International Conference on Data Mining, pages 289–296, November, 2001. doi abstract bibtex 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.
@inproceedings{keogh_online_2001,
title = {An online algorithm for segmenting time series},
doi = {10.1109/ICDM.2001.989531},
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.},
booktitle = {Proceedings 2001 {IEEE} {International} {Conference} on {Data} {Mining}},
author = {Keogh, E. and Chu, S. and Hart, D. and Pazzani, M.},
month = nov,
year = {2001},
keywords = {Association rules, Change detection algorithms, Clustering algorithms, Computer science, Data mining, Databases, Explosions, Indexing, Piecewise linear approximation, Piecewise linear techniques, association rule mining, classification, clustering, data mining, data representation, empirical comparison, indexing, online algorithm, online operation, piecewise linear approximation, piecewise linear techniques, review, reviews, time series, time series segmentation, time-series database mining},
pages = {289--296},
}
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