Advertisement

MLSP: Mining Hierarchically-Closed Multi-Level Sequential Patterns

  • Michal Šebek
  • Martin Hlosta
  • Jaroslav Zendulka
  • Tomáš Hruška
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8346)

Abstract

The problem of mining sequential patterns has been widely studied and many efficient algorithms used to solve this problem have been published. In some cases, there can be implicitly or explicitely defined taxonomies (hierarchies) over input items (e.g. product categories in a e-shop or sub-domains in the DNS system). However, how to deal with taxonomies in sequential pattern mining is marginally discussed. In this paper, we formulate the problem of mining hierarchically-closed multi-level sequential patterns and demonstrate its usefulness. The MLSP algorithm based on the on-demand generalization that outperforms other similar algorithms for mining multi-level sequential patterns is presented here.

Keywords

closed sequential pattern mining taxonomy generalization GSP MLSP 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Agrawal, R., Srikant, R.: Mining sequential patterns. In: Proc. of the Eleventh International Conference on Data Engineering, pp. 3–14 (March 1995)Google Scholar
  2. 2.
    Han, J., Fu, A.: Mining multiple-level association rules in large databases. IEEE Trans. on Knowledge and Data Engineering 11(5), 798–805 (1999)CrossRefGoogle Scholar
  3. 3.
    Han, J., Kamber, M.: Data mining: concepts and techniques. The Morgan Kaufmann series in data management systems. Elsevier (2006)Google Scholar
  4. 4.
    Nakano, S.I.: Efficient generation of plane trees. Inf. Process. Lett. 84(3), 167–172 (2002)CrossRefzbMATHGoogle Scholar
  5. 5.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: VLDB 1994, pp. 487–499 (1994)Google Scholar
  6. 6.
    Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. SIGMOD Rec. 29(2), 1–12 (2000)CrossRefGoogle Scholar
  7. 7.
    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. 1–17. Springer, Heidelberg (1996)CrossRefGoogle Scholar
  8. 8.
    Zaki, M.: SPADE: An efficient algorithm for mining frequent sequences. Machine Learning 42, 31–60 (2001)CrossRefzbMATHGoogle Scholar
  9. 9.
    Ayres, J., Flannick, J., Gehrke, J., Yiu, T.: Sequential pattern mining using a bitmap representation. In: Proc. of the 8th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, pp. 429–435. ACM, New York (2002)Google Scholar
  10. 10.
    Pei, J., Han, J., Mortazavi-Asl, B., Wang, J., Pinto, H., Chen, Q., Dayal, U., Hsu, M.C.: Mining sequential patterns by pattern-growth: the prefixspan approach. IEEE Trans. on Knowledge and Data Engineering 16(11), 1424–1440 (2004)CrossRefGoogle Scholar
  11. 11.
    Wang, J., Han, J.: BIDE: efficient mining of frequent closed sequences. In: Proc. of 20th International Conference on Data Engineering, pp. 79–90 (2004)Google Scholar
  12. 12.
    Plantevit, M., Laurent, A., Laurent, D., Teisseire, M., Choong, Y.W.: Mining multidimensional and multilevel sequential patterns. ACM Trans. Knowl. Discov. Data 4(1), 4:1–4:37 (2010)Google Scholar
  13. 13.
    Šebek, M., Hlosta, M., Kupčík, J., Zendulka, J., Hruška, T.: Multi-level sequence mining based on gsp. Acta Electrotechnica et Informatica (2), 31–38 (2012)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Michal Šebek
    • 1
  • Martin Hlosta
    • 1
  • Jaroslav Zendulka
    • 1
  • Tomáš Hruška
    • 1
  1. 1.Faculty of Information Technology, IT4Innovations Centre of ExcellenceBrno University of TechnologyBrnoCzech Republic

Personalised recommendations