Advertisement

Mining Patterns with Durations from E-Commerce Dataset

  • Mohamad Kanaan
  • Hamamache Kheddouci
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 812)

Abstract

Given a dataset of clickstream extracted from e-commerce logs, can we find a clear usage of the website? Are there hidden relationships between the purchased products? Are there any discriminatory behaviors leading to the purchase? To answer these questions, we propose in this paper a new Sequential Event Pattern Mining algorithm (SEPM). The endeavor is to mine clickstream data in order to extract and analyze useful sequential patterns of clicks. Also, in order to make these patterns clearer, the time spent on each page is taken into account. SEPM maintains the items durations during the mining process and extracts patterns with the average durations of these items without multiple scans of the dataset. Our experimental results on both real and synthetic datasets indicate that SEPM is efficient and scalable.

Keywords

Data mining Frequent pattern Customer behavior E-commerce 

References

  1. 1.
    Zaki, M.J.: SPADE: an efficient algorithm for mining frequent sequences. Mach. Learn. 42(1), 31–60 (2001)Google Scholar
  2. 2.
    Han, J., Pei, J., Mortazavi-Asl, B., Chen, Q., Dayal, U., Hsu, M.C.: FreeSpan: frequent pattern-projected sequential pattern mining. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 355–359 (2000)Google Scholar
  3. 3.
    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
  4. 4.
    Srikant, R., Agrawal, R.: Mining sequential patterns: generalizations and performance improvements. In: The International Conference on Extending Database Technology, pp. 1–17 (1996)Google Scholar
  5. 5.
    Agrawal, R., Srikant, R.: Mining sequential patterns. In: Proceedings of the 11th International Conference on Data Engineering, Taipei, Taiwan, March 1995Google Scholar
  6. 6.
    Han, J., Pei, J., Ying, Y., Mao, R.: Mining frequent patterns without candidate generation: a frequent-pattern tree approach. Data Min. Knowl. Discov. 8(1), 53–87 (2004)Google Scholar
  7. 7.
    Fournier-Viger, P., Lin, J.C.W., Kiran, R.U., Koh, Y.S., Thomas, R.: A survey of sequential pattern mining. Data Sci. Pattern Recognit. 1(1), 54–77 (2017)Google Scholar
  8. 8.
  9. 9.
  10. 10.
  11. 11.

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Sistema StrategyLyonFrance
  2. 2.Université Claude Bernard Lyon 1, Laboratoire LIRISLyonFrance

Personalised recommendations