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Ramex: A Sequence Mining Algorithm Using Poly-trees

  • Luís CaviqueEmail author
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 354)

Abstract

Sequence mining combines the discovery of frequent itemsets and the order they appear in. Most of the sequence pattern discovery techniques present some handicaps like the generation of a huge number of rules and the lack of scalability. In this work the proposed algorithm concerns the analysis of the whole rather than the parts, thus providing a holistic view of the sequences. The algorithm analyzes event logs and allows a non-expert user to understand the sequences using a poly-tree visualization. The scalability associated with condensed data structures, which shrink the data without losing information, allows dealing with the Big Data challenge. Ramex was implemented in different scenarios.

Keywords

pervasive business intelligence sequence mining poly-trees 

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References

  1. 1.
    van der Aalst, W.: Process Mining - Discovery, Conformance and Enhancement of Business Processes. Springer (2011) ISBN 978-3-642-19344-6Google Scholar
  2. 2.
    van der Aalst, W.: Mine Your Own Business: Using Process Mining to Turn Big Data into Real Value. In: Keynote 21st European Conference on Information Systems, ECIS, Utrecht, The Netherlands (2013)Google Scholar
  3. 3.
    van der Aalst, W., et al.: Process mining manifesto. In: Daniel, F., Barkaoui, K., Dustdar, S. (eds.) BPM Workshops 2011, Part I. LNBIP, vol. 99, pp. 169–194. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  4. 4.
    Agrawal, R., Srikant, R.: Mining sequential patterns. In: Proceedings of the 11th International Conference Data Engineering, ICDE, pp. 3–14. IEEE Press (1995)Google Scholar
  5. 5.
    Borges, J., Levene, M.: Evaluating Variable-Length Markov Chain Models for Analysis of User Web Navigation Sessions. IEEE Trans. Knowl. Data Eng. 19(4), 441–452 (2007)CrossRefGoogle Scholar
  6. 6.
    Cavique, L.: A Network Algorithm to Discover Sequential Patterns. In: Neves, J., Santos, M.F., Machado, J.M. (eds.) EPIA 2007. LNCS (LNAI), vol. 4874, pp. 406–414. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  7. 7.
    Cavique, L.: A new taxonomy in Data Science. Maximus Report, section IV, pp. 92–93 (2014) (in Portuguese)Google Scholar
  8. 8.
    Cavique, L., Coelho, J.: Sequential Pattern Discovery Using Oriented Trees. Revista de Ciências da Computação (3), 12–22 (2008) (in Portuguese)Google Scholar
  9. 9.
    Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C.: Introduction to Algorithms, 3rd edn. MIT Press and McGraw-Hill (2009) ISBN 0-262-03384-4Google Scholar
  10. 10.
    Edmonds, J.: Optimum branchings. J. Research of the National Bureau of Standards 71B, 233–240 (1967)CrossRefMathSciNetGoogle Scholar
  11. 11.
    Fulkerson, D.R.: Packing rooted directed cuts in a weighted directed graph. Mathematical Programming 6, 1–13 (1974)CrossRefzbMATHMathSciNetGoogle Scholar
  12. 12.
    GraphViz (2014), http://www.graphviz.org/ (accessed June 9, 2014)
  13. 13.
    IBM Almaden Research Center, Synthetic data generation code for associations and sequential patterns (2006), http://www.almaden.ibm.com/software/quest/
  14. 14.
    Knuth, D.E., Morris, J.H., Pratt, V.R.: Fast pattern matching in strings. SIAM Journal on Computing 6(1), 323–350 (1977)CrossRefzbMATHMathSciNetGoogle Scholar
  15. 15.
    Mannila, H., Toivonen, H., Verkamo, I.: Discovery of frequent episodes in event sequences. Data Mining and Knowledge Discovery 1(3), 259–289 (1997)CrossRefGoogle Scholar
  16. 16.
    Marques, N.C., Cavique, L.: Sequential pattern mining of price interactions. In: EPIA 2013, 16th Portuguese Conference, Advances in Artificial Intelligence, Local Proceedings, Angra do Heroísmo, Açores, Portugal, pp. 314–325 (2013)Google Scholar
  17. 17.
    McKendrick, J.: Pervasive Business Intelligence means BI for the masses. Informatica (2008), http://blogs.informatica.com/perspectives/2008/08/31/pervasive-business-intelligence-means-bi-for-the-masses/#fbid=k4I_3ZvQSUp (accessed December 15, 2014)
  18. 18.
    Rebane, G., Pearl, J.: The recovery of causal poly-trees from statistical data. In: Proceedings of Uncertainty in Artificial Intelligence, pp. 222–228 (1987)Google Scholar
  19. 19.
    Srikant, R., Agrawal, R.: Mining sequential patterns: Generalizations and performance improvements. In: Apers, P.M.G., Bouzeghoub, M., Gardarin, G. (eds.) EDBT 1996. LNCS, vol. 1057, pp. 3–17. Springer, Heidelberg (1996)Google Scholar
  20. 20.
    Tiple, P.S.: Tool for Discovering Sequential Patterns in Financial Markets. Dissertação para obtenção do Grau de Mestre em Engenharia Informática, na Faculdade de Ciências e Tecnologia da Universidade Nova Lisboa (2014)Google Scholar
  21. 21.
    Tulip, Better Visualization Through Research (2014), http://tulip.labri.fr/TulipDrupal (accessed December 2014)
  22. 22.
    Wang, W., Yang, J., Yu, P.: Meta-Patterns: revealing hidden periodic patterns. In: IEEE International Conference on Data Mining (ICDM), pp. 550–557 (2001)Google Scholar
  23. 23.
    Zaki, M.J.: Spade: An efficient algorithm for mining frequent sequences. Machine Learning 42, 31–60 (2001)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Departamento de Ciências e TecnologiaUniversidade AbertaLisbonPortugal

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