SPaR-FTR: An Efficient Algorithm for Mining Sequential Patterns-Based Rules

  • José Kadir Febrer-HernándezEmail author
  • Raudel Hernández-León
  • José Hernández-Palancar
  • Claudia Feregrino-Uribe
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9423)


In this paper, we propose a novel algorithm for mining Sequential Patterns-based Rules, called SPaR-FTR. This algorithm introduces a new efficient strategy to generate the set of sequential rules based on the interesting rules of size three. The experimental results show that the SPaR-FTR algorithm has better performance than the main algorithms reported to discover frequent sequences, all they adapted to mine this kind of sequential rules.


Data mining Sequential patterns Rule mining 


  1. 1.
    Buddeewong, S., Kreesuradej, W.: A new association rule-based text classifier algorithm. In: Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence, pp. 684–685 (2005)Google Scholar
  2. 2.
    Xei, F., Wu, X., Zhu, X.: Document-specific keyphrase extraction using sequential patterns with wildcards. In: Proceedings of the IEEE 14th International Conference on Data Mining (2014)Google Scholar
  3. 3.
    Cesario, E., Folino, F., Locane, A., Manco, G., Ortale, R.: Boosting text segmentation via progressive classification. Knowl. Inf. Syst. 15(3), 285–320 (2008)CrossRefGoogle Scholar
  4. 4.
    García-Hernández, R.A., Martínez-Trinidad, J.F., Carrasco-Ochoa, J.A.: A fast algorithm to find all the maximal frequent sequences in a text. In: Sanfeliu, A., Martínez Trinidad, J.F., Carrasco Ochoa, J.A. (eds.) CIARP 2004. LNCS, vol. 3287, pp. 478–486. Springer, Heidelberg (2004) CrossRefGoogle Scholar
  5. 5.
    Shettar, R.: Sequential Pattern Mining from Web Log Data. International Journal of Engineering Science and Advanced Technology 2, 204–208 (2012)Google Scholar
  6. 6.
    Haleem, H., Kumar, P., Beg, S.: Novel frequent sequential patterns based probabilistic model for effective classification of web documents. In: 2014 International Conference on Computer and Communication Technology (ICCCT), pp. 361–371 (2014)Google Scholar
  7. 7.
    Berzal, F., Cubero, J.C., Sánchez, D., Serrano, J.M.: ART: A Hybrid Classification Model. Mach. Learn. 54(1), 67–92 (2004)CrossRefzbMATHGoogle Scholar
  8. 8.
    Faghihi, U., Fournier-Viger, P., Nkambou, R., Poirier, P.: A generic episodic learning model implemented in a cognitive agent by means of temporal pattern mining. In: Chien, B.-C., Hong, T.-P., Chen, S.-M., Ali, M. (eds.) IEA/AIE 2009. LNCS, vol. 5579, pp. 545–555. Springer, Heidelberg (2009) CrossRefGoogle Scholar
  9. 9.
    Teredesai, A.M., Ahmad, M.A., Kanodia, J., Gaborski, R.S.: CoMMA: A Framework for Integrated Multimedia Mining Using Multi-relational Associations. Knowl. Inf. Syst. 10(2), 135–162 (2006)CrossRefGoogle Scholar
  10. 10.
    Agrawal, R., Srikant, R.: Mining sequential patterns. In: Proceedings of the Eleventh International Conference on Data Engineering, pp. 3–14 (1995)Google Scholar
  11. 11.
    Srikant, R., Agrawal, R.: Mining sequential patterns: generalizations and performance improvements. In: Proceedings in the 5th International Conference Extending Database Technology, pp. 3–17 (1996)Google Scholar
  12. 12.
    Garofalakis, M., Rastogi, R., Shim, K.: SPIRIT: Sequential pattern mining with regular expression constraints. In: Proceedings of the 25th International Conference on Very Large Data Bases, pp. 223–234 (1999)Google Scholar
  13. 13.
    Pei, J., Han, J., Mortazavi-asl, B., Pinto, H., Chen, Q., Dayal U., Hsu, M.: PrefixSpan: Mining sequential patterns efficiently by prefix-projected pattern growth. In: Proceedings of the 17th International Conference on Data Engineering, pp. 215–224 (2001)Google Scholar
  14. 14.
    Yang, Z., Wang, Y., Kitsuregawa, M.: LAPIN: effective sequential pattern mining algorithms by last position induction for dense databases. In: Kotagiri, R., Radha Krishna, P., Mohania, M., Nantajeewarawat, E. (eds.) DASFAA 2007. LNCS, vol. 4443, pp. 1020–1023. Springer, Heidelberg (2007) CrossRefGoogle Scholar
  15. 15.
    Gouda, K., Hassaan, M., Zaki, M.J.: Prism: An effective approach for frequent sequence mining via prime-block encoding. J. Comput. Syst. Sci. 76(1), 88–102 (2010)MathSciNetCrossRefzbMATHGoogle Scholar
  16. 16.
    Yu, X., Li, M., Lee, D.G., Kim, K.D., Ryu, K.H.: Application of closed gap-constrained sequential pattern mining in web log data. In: Zeng, D. (ed.) Advances in Control and Communication, LNEE, vol. 137, pp. 649–656. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  17. 17.
    Liao, V., Chen, M.: An efficient sequential pattern mining algorithm for motifs with gap constraints. In: Proceedings of the 2012 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • José Kadir Febrer-Hernández
    • 1
    Email author
  • Raudel Hernández-León
    • 1
  • José Hernández-Palancar
    • 1
  • Claudia Feregrino-Uribe
    • 2
  1. 1.Centro de Aplicaciones de Tecnologías de Avanzada (CENATAV)PlayaCuba
  2. 2.Instituto Nacional de Astrofísica, Óptica y Electrónica (INAOE)PueblaMexico

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