Variance-wise segmentation for a temporal-adaptive SAX

Authors:
Chao Sun;David Stirling;Christian Ritz;Claude Sammut
Affiliations:
University of Wollongong, Wollongong NSW, Australia;University of Wollongong, Wollongong NSW, Australia;University of Wollongong, Wollongong NSW, Australia;The University of New South Wales, Sydney, Australia
Venue:
AusDM '12 Proceedings of the Tenth Australasian Data Mining Conference - Volume 134
Year:
2012

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Abstract

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.