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Variance-wise Segmentation for a Temporal-Adaptive SAX
Sun, C., Stirling, D., Ritz, C., and Sammut, C.
The Symbolic Aggregate approXimation algorithm (SAX) is a very popular symbolic mapping technique for time series data, and it is widely employed in pattern identification, sequence classification, abnormality detection and other data mining research. Although SAX is a general approach which is adaptable to most data, it utilises a fixed-size sliding window in order to generate motifs (temporal shapes). When certain target phenomena (activities of interest) are manifested over differing time scales, SAX-motifs are unable to correctly account for all such targets. This paper proposes a new method named the variance-wise segmentation method which can adaptively change the size of the sliding window in a generalised SAX approach. By generating motifs with differing durations, patterns can be found for activities with similar shape but occurring over a significant altered time base. This method is tested on both artificially modified ECG data, as well as, variable tactile vibration data, with improved results compared to the original SAX formulation. |
Cite as: Sun, C., Stirling, D., Ritz, C., and Sammut, C. (2012). Variance-wise Segmentation for a Temporal-Adaptive SAX. In Proc. Data Mining and Analytics 2012 (AusDM 2012) Sydney, Australia. CRPIT, 134. Zhao, Y., Li, J. , Kennedy, P.J. and Christen, P. Eds., ACS. 71 - 78 |
(from crpit.com)
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