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GrammarViz 2.0: A Tool for Grammar-Based Pattern Discovery in Time Series

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 8726)

Abstract

The problem of frequent and anomalous patterns discovery in time series has received a lot of attention in the past decade. Addressing the common limitation of existing techniques, which require a pattern length to be known in advance, we recently proposed grammar-based algorithms for efficient discovery of variable length frequent and rare patterns. In this paper we present GrammarViz 2.0, an interactive tool that, based on our previous work, implements algorithms for grammar-driven mining and visualization of variable length time series patterns1.

Keywords

  • Context Free Grammar
  • Pattern Discovery
  • Grammar Rule
  • Input Time Series
  • Merging Operator

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

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Senin, P. et al. (2014). GrammarViz 2.0: A Tool for Grammar-Based Pattern Discovery in Time Series. In: Calders, T., Esposito, F., Hüllermeier, E., Meo, R. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2014. Lecture Notes in Computer Science(), vol 8726. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44845-8_37

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  • DOI: https://doi.org/10.1007/978-3-662-44845-8_37

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-44844-1

  • Online ISBN: 978-3-662-44845-8

  • eBook Packages: Computer ScienceComputer Science (R0)