SAX (Symbolic Aggregate approXimation):
SAX is the
first symbolic representation for time series that allows for dimensionality
reduction and indexing with a lower-bounding distance measure.
In classic data
mining tasks such as clustering, classification, index, etc., SAX is
as good as well-known representations such as Discrete Wavelet Transform (DWT)
and Discrete Fourier Transform (DFT), while requiring less storage space.
In addition, the representation allows researchers to avail of the wealth of
data structures and algorithms in bioinformatics or text mining, and also provides solutions to many
challenges associated with current data mining tasks. One example is motif
discovery, a problem which we recently defined for time series data. There
is great potential for extending and applying the discrete representation on
a wide class of data mining tasks.
Download SAX.ppt: This presentation may be useful to gain some intuition into the utility of SAX.
For an updated, more complete citation list, see Google Scholar:
Page last updated: Aug 24, 2011