San Jose, CA, USA
Nov. 29, 2001 to Dec. 2, 2001
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 t me 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 super or to all others n the literature.
Eamonn Keogh, Selina Chu, David Hart, Michael Pazzani, "An Online Algorithm for Segmenting Time Series", ICDM, 2001, Proceedings 2001 IEEE International Conference on Data Mining, Proceedings 2001 IEEE International Conference on Data Mining 2001, pp. 289, doi:10.1109/ICDM.2001.989531