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Springer-Verlag, Berlin, Heidelberg, Pages 461-477. The 21st International Conference on Scientific and Statisticalĭatabase Management (SSDBM 2009), Marianne Winslett

Series data using Bag-of-Patterns representation. Nearest Neighbor Search in Multimedia Data. (2007).Įxperiencing SAX: a Novel Symbolic Representation of Time Series. Lecture Notes in Computer Science, Springer. In Proceedings of the 10th European Conference on Principles and Practice of Knowledge Discovery in Databases. Group SAX: Extending the notion of contrast sets to time series and multimedia data. Subsequence: Algorithms and Applications. of the 5th IEEE InternationalĬonference on Data Mining (ICDM 2005), pp.

Proceedings of the tenth ACM SIGKDD International Conference on Visually Mining and Monitoring Massive Time Series.

Is great potential for extending and applying the discrete representation onĭownload SAX.ppt: This presentation may be useful to gain some intuition into the utility of SAX. One example is motifĭiscovery, a problem which we recently defined for time series data. In addition, the representation allows researchers to avail of the wealth ofĭata structures and algorithms in bioinformatics or text mining, and also provides solutions to manyĬhallenges associated with current data mining tasks. Mining tasks such as clustering, classification, index, etc., SAX isĪs good as well-known representations such as Discrete Wavelet Transform (DWT)Īnd Discrete Fourier Transform (DFT), while requiring less storage space. Reduction and indexing with a lower-bounding distance measure. SAX ( Symbolic Aggregate appro Ximation):įirst symbolic representation for time series that allows for dimensionality