Database Reference
In-Depth Information
Space Shuttle
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Fig. 5. A comparison of the three major times series segmentation algorithms, on ten
diverse datasets, over a range in parameters. Each experimental result (i.e. a triplet of
histogram bars) is normalized by dividing by the performance of the worst algorithm on
that experiment.
approaches produce better results, but are oine and require the scan-
ning of the entire data set. This is impractical or may even be unfeasible in
a data-mining context, where the data are in the order of terabytes or arrive
in continuous streams. We therefore introduce a novel approach in which
we capture the online nature of Sliding Windows and yet retain the supe-
riority of Bottom-Up. We call our new algorithm SWAB (Sliding Window
and Bottom-up).
4.1.
The SWAB Segmentation Algorithm
The SWAB algorithm keeps a buffer of size
. The buffer size should ini-
tially be chosen so that there is enough data to create about 5 or 6 segments.
w
