Fast Vertical Mining of Sequential Patterns Using Co-occurrence Information

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8443)


Sequential pattern mining algorithms using a vertical representation are the most efficient for mining sequential patterns in dense or long sequences, and have excellent overall performance. The vertical representation allows generating patterns and calculating their supports without performing costly database scans. However, a crucial performance bottleneck of vertical algorithms is that they use a generate-candidate-and-test approach that can generate a large amount of infrequent candidates.To address this issue, we propose pruning candidates based on the study of item co-occurrences. We present a new structure named CMAP (Co-occurence MAP) for storing co-occurrence information. We explain how CMAP can be used to prune candidates in three state-of-the-art vertical algorithms, namely SPADE, SPAM and ClaSP. An extensive experimental study with six real-life datasets shows that (1) co-occurrence-based pruning is effective, (2) CMAP is very compact and that (3) the resulting algorithms outperform state-of-the-art algorithms for mining sequential patterns (GSP, PrefixSpan, SPADE and SPAM) and closed sequential patterns (ClaSP and CloSpan).


sequential pattern mining vertical database format candidate pruning 


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  1. 1.
    Agrawal, R., Ramakrishnan, S.: Mining sequential patterns. In: Proc. 11th Intern. Conf. Data Engineering, pp. 3–14. IEEE (1995)Google Scholar
  2. 2.
    Aseervatham, S., Osmani, A., Viennet, E.: bitSPADE: A Lattice-based Sequential Pattern Mining Algorithm Using Bitmap Representation. In: Proc. 6th Intern. Conf. Data Mining, pp. 792–797. IEEE (2006)Google Scholar
  3. 3.
    Ayres, J., Flannick, J., Gehrke, J., Yiu, T.: Sequential pattern mining using a bitmap representation. In: Proc. 8th ACM SIGKDD Intern. Conf. Knowledge Discovery and Data Mining, pp. 429–435. ACM (2002)Google Scholar
  4. 4.
    Fournier-Viger, P., Gomariz, A., Gueniche, T., Mwamikazi, E., Thomas, R.: TKS: Efficient Mining of Top-K Sequential Patterns. In: Motoda, H., Wu, Z., Cao, L., Zaiane, O., Yao, M., Wang, W. (eds.) ADMA 2013, Part I. LNCS, vol. 8346, pp. 109–120. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  5. 5.
    Fournier-Viger, P., Nkambou, R., Tseng, V.S.: RuleGrowth: Mining Sequential Rules Common to Several Sequences by Pattern-Growth. In: Proc. ACM 26th Symposium on Applied Computing, pp. 954–959 (2011)Google Scholar
  6. 6.
    Fournier-Viger, P., Wu, C.-W., Tseng, V.S.: Mining Maximal Sequential Patterns without Candidate Maintenance. In: Motoda, H., Wu, Z., Cao, L., Zaiane, O., Yao, M., Wang, W. (eds.) ADMA 2013, Part I. LNCS, vol. 8346, pp. 169–180. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  7. 7.
    Gomariz, A., Campos, M., Marin, R., Goethals, B.: ClaSP: An Efficient Algorithm for Mining Frequent Closed Sequences. In: Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu, G. (eds.) PAKDD 2013, Part I. LNCS, vol. 7818, pp. 50–61. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  8. 8.
    Han, J., Kamber, M.: Data Mining: Concepts and Techniques, 2nd edn. Morgan Kaufmann, San Francisco (2006)Google Scholar
  9. 9.
    Mabroukeh, N.R., Ezeife, C.I.: A taxonomy of sequential pattern mining algorithms. ACM Computing Surveys 43(1), 1–41 (2010)CrossRefGoogle Scholar
  10. 10.
    Pei, J., Han, J., Mortazavi-Asl, B., Wang, J., Pinto, H., Chen, Q., Dayal, U., Hsu, M.: Mining sequential patterns by pattern-growth: the PrefixSpan approach. IEEE Trans. Knowledge Data Engineering 16(11), 1424–1440 (2004)CrossRefGoogle Scholar
  11. 11.
    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
  12. 12.
    Yan, X., Han, J., Afshar, R.: CloSpan: Mining closed sequential patterns in large datasets. In: Proc. 3rd SIAM Intern. Conf. on Data Mining, pp. 166–177 (2003)Google Scholar
  13. 13.
    Zaki, M.J.: SPADE: An efficient algorithm for mining frequent sequences. Machine Learning 42(1), 31–60 (2001)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Dept. of Computer ScienceUniversity of MonctonCanada
  2. 2.Dept. of Information and Communication EngineeringUniversity of MurciaSpain
  3. 3.Dept. of Computer ScienceSCTBhopalIndia

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