An online algorithm for segmenting time series
University of California, Irvine
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…
Citation impact
- FWCI
- 25.75
- Percentile
- 100%
- References
- 39
Authors
4Topics & keywords
- Computer science
- Search engine indexing
- Representation (politics)
- Series (stratigraphy)
- Cluster analysis
- Data mining
- Time series
- Variety (cybernetics)