Frequent Temporal Pattern Mining with Extended Lists

  • A. Kocheturov
  • P. M. Pardalos


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.



Research was supported by RSF grant 14-41-00039.


  1. 1.
    R. Agrawal, R. Srikant, Mining sequential patterns, in Proceedings of the Eleventh International Conference on Data Engineering (1995), pp. 3–14Google Scholar
  2. 2.
    J.F. Allen, Towards a general theory of action and time. Artif. Intell. 23(2), 123–154 (1984)CrossRefGoogle Scholar
  3. 3.
    J. Ayres, J. Flannick, J. Gehrke, T. Yiu, Sequential pattern mining using a bitmap representation, in Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (ACM, New York, 2002), pp. 429–435Google Scholar
  4. 4.
    I. Batal, L. Sacchi, R. Bellazzi, M. Hauskrecht, Multivariate time series classification with temporal abstractions, Proceedings of the 22nd International Artificial Intelligence Research Society Conference (FLAIRS - 22) (2009), pp. 344–349Google Scholar
  5. 5.
    I. Batal, H. Valizadegan, G.F. Cooper, M. Hauskrecht, A pattern mining approach for classifying multivariate temporal data, in 2011 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (2011), pp. 358–365Google Scholar
  6. 6.
    I. Batal, D. Fradkin, J. Harrison, F. Moerchen, M. Hauskrecht, Mining recent temporal patterns for event detection in multivariate time series data, in Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2012), pp. 280–288Google Scholar
  7. 7.
    I. Batal, G.F. Cooper, D. Fradkin, J. Harrison Jr, F. Moerchen, M. Hauskrecht, An efficient pattern mining approach for event detection in multivariate temporal data. Knowl. Inf. Syst. 46(1), 115–150 (2016)CrossRefGoogle Scholar
  8. 8.
    D.-Y. Chiu, Y.-H. Wu, A.L.P. Chen, An efficient algorithm for mining frequent sequences by a new strategy without support counting, in Proceedings of the 20th International Conference on Data Engineering (IEEE, Piscataway, 2004), pp. 375–386Google Scholar
  9. 9.
    J. Han, J. Pei, B. Mortazavi-Asl, H. Pinto, Q. Chen, U. Dayal, M.C. Hsu, Prefixspan: mining sequential patterns efficiently by prefix-projected pattern growth, in Proceedings of the 17th International Conference on Data Engineering, 2001, pp. 215–224Google Scholar
  10. 10.
    M. Hauskrecht, S. Visweswaran, G.F. Cooper, G. Clermont, Data-driven identification of unusual clinical actions in the ICU, in AMIA (2013)Google Scholar
  11. 11.
    D. Korenkevych, T. Ozrazgat-Baslanti, P. Thottakkara, C.E. Hobson, P. Pardalos, P. Momcilovic, A. Bihorac, The pattern of longitudinal change in serum creatinine and 90-day mortality after major surgery. Ann. Surg. 263(6), 1219–1227 (2016)CrossRefGoogle Scholar
  12. 12.
    R. Moskovitch, Y. Shahar, Classification-driven temporal discretization of multivariate time series. Data Min. Knowl. Disc. 29(4), 871–913 (2015)MathSciNetCrossRefGoogle Scholar
  13. 13.
    L. Sacchi, C. Larizza, C. Combi, R. Bellazzi, Data mining with temporal abstractions: learning rules from time series. Data Min. Knowl. Disc. 15(2), 217–247 (2007)MathSciNetCrossRefGoogle Scholar
  14. 14.
    R. Srikant, R. Agrawal, Mining sequential patterns: generalizations and performance improvements, in Advances in Database Technology—EDBT’96 (1996), pp. 1–17Google Scholar
  15. 15.
    P. Thottakkara, T. Ozrazgat-Baslanti, B.B. Hupf, P. Rashidi, P. Pardalos, P. Momcilovic, A. Bihorac, Application of machine learning techniques to high-dimensional clinical data to forecast postoperative complications. PLoS One 11(5), e0155705 (2016)Google Scholar
  16. 16.
    J. Wang, J. Han, Bide: efficient mining of frequent closed sequences, in Proceedings of the 20th International Conference on Data Engineering (IEEE, Piscataway, 2004), pp. 79–90CrossRefGoogle Scholar
  17. 17.
    M.J. Zaki, Scalable algorithms for association mining. IEEE Trans. Knowl. Data Eng. 12(3), 372–390 (2000)CrossRefGoogle Scholar
  18. 18.
    M.J. Zaki, Spade: an efficient algorithm for mining frequent sequences. Mach. Learn. 42(1), 31–60 (2001)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • A. Kocheturov
    • 1
  • P. M. Pardalos
    • 1
    • 2
  1. 1.Center for Applied Optimization (CAO)University of FloridaGainesvilleUSA
  2. 2.Laboratory of Algorithms and Technologies for Networks Analysis (LATNA)National Research University, Higher School of EconomicsMoscowRussia

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