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Frequent Temporal Pattern Mining with Extended Lists

In: Trends in Biomathematics: Modeling, Optimization and Computational Problems

Author

Listed:
  • A. Kocheturov

    (University of Florida, Center for Applied Optimization (CAO))

  • P. M. Pardalos

    (University of Florida, Center for Applied Optimization (CAO)
    National Research University, Higher School of Economics, Laboratory of Algorithms and Technologies for Networks Analysis (LATNA))

Abstract

In this paper we consider Temporal Pattern Mining (TPM) for extracting predictive class-specific patterns from multivariate time series. We suggest a new approach that extends usage of the a priori property which requires a more complex pattern to appear only at places where all its subpatterns appear as well. It is based on tracking positions of a pattern inside records in a greedy manner. We demonstrate that it outperforms the previous version of the TMP on several real-life data sets independent of the way how the temporal pattern is defined.

Suggested Citation

  • A. Kocheturov & P. M. Pardalos, 2018. "Frequent Temporal Pattern Mining with Extended Lists," Springer Books, in: Rubem P. Mondaini (ed.), Trends in Biomathematics: Modeling, Optimization and Computational Problems, pages 233-244, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-91092-5_16
    DOI: 10.1007/978-3-319-91092-5_16
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