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VMSP: Efficient Vertical Mining of Maximal Sequential Patterns

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Advances in Artificial Intelligence (Canadian AI 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8436))

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Abstract

Sequential pattern mining is a popular data mining task with wide applications. However, it may present too many sequential patterns to users, which makes it difficult for users to comprehend the results. As a solution, it was proposed to mine maximal sequential patterns, a compact representation of the set of sequential patterns, which is often several orders of magnitude smaller than the set of all sequential patterns. However, the task of mining maximal patterns remains computationally expensive. To address this problem, we introduce a vertical mining algorithm named VMSP (Vertical mining of Maximal Sequential Patterns). It is to our knowledge the first vertical mining algorithm for mining maximal sequential patterns. An experimental study on five real datasets shows that VMSP is up to two orders of magnitude faster than the current state-of-the-art algorithm.

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References

  1. Agrawal, R., Ramakrishnan, S.: Mining sequential patterns. In: Proc. 11th Intern. Conf. Data Engineering, pp. 3–14. IEEE (1995)

    Google Scholar 

  2. Ayres, J., Flannick, J., Gehrke, J., Yiu, T.: Sequential pattern mining using a bitmap representation. In: Proc. 8th ACM Intern. Conf. Knowl. Discov. Data Mining, pp. 429–435. ACM (2002)

    Google Scholar 

  3. 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 (LNAI), vol. 8346, pp. 169–180. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  4. Fournier-Viger, P., Gomariz, A., Campos, M., Thomas, R.: Fast Vertical Sequential Pattern Mining Using Co-occurrence Information. In: Proc. 18th Pacific-Asia Conference on Knowledge Discovery and Data Mining. LNCS (LNAI). Springer (2014)

    Google Scholar 

  5. García-Hernández, R.A., Martínez-Trinidad, J.F., Carrasco-Ochoa, J.A.: A new algorithm for fast discovery of maximal sequential patterns in a document collection. In: Gelbukh, A. (ed.) CICLing 2006. LNCS, vol. 3878, pp. 514–523. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  6. 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)

    Chapter  Google Scholar 

  7. Guan, E.-Z., Chang, X.-Y., Wang, Z., Zhou, C.-G.: Mining Maximal Sequential Patterns. In: Proc. 2nd Intern. Conf. Neural Networks and Brain, pp. 525–528 (2005)

    Google Scholar 

  8. Lin, N.P., Hao, W.-H., Chen, H.-J., Chueh, H.-E., Chang, C.-I.: Fast Mining Maximal Sequential Patterns. In: Proc. of the 7th Intern. Conf. on Simulation, Modeling and Optimization, Beijing, China, September 15-17, pp. 405–408 (2007)

    Google Scholar 

  9. Luo, C., Chung, S.: Efficient mining of maximal sequential patterns using multiple samples. In: Proc. 5th SIAM Intern. Conf. Data Mining, Newport Beach, CA (2005)

    Google Scholar 

  10. Lu, S., Li, C.: Apriori Adjust: An Efficient Algorithm for Discovering the Maximum Sequential Patterns. In: Proc. Intern. Workshop Knowl. Grid and Grid Intell. (2004)

    Google Scholar 

  11. Mabroukeh, N.R., Ezeife, C.I.: A taxonomy of sequential pattern mining algorithms. ACM Computing Surveys 43(1), 1–41 (2010)

    Article  Google Scholar 

  12. 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. Known. Data Engin. 16(11), 1424–1440 (2004)

    Article  Google Scholar 

  13. Wang, J., Han, J., Li, C.: Frequent closed sequence mining without candidate maintenance. IEEE Trans. on Knowledge Data Engineering 19(8), 1042–1056 (2007)

    Article  MathSciNet  Google Scholar 

  14. Zaki, M.J.: SPADE: An efficient algorithm for mining frequent sequences. Machine Learning 42(1), 31–60 (2001)

    Article  MATH  Google Scholar 

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Fournier-Viger, P., Wu, CW., Gomariz, A., Tseng, V.S. (2014). VMSP: Efficient Vertical Mining of Maximal Sequential Patterns. In: Sokolova, M., van Beek, P. (eds) Advances in Artificial Intelligence. Canadian AI 2014. Lecture Notes in Computer Science(), vol 8436. Springer, Cham. https://doi.org/10.1007/978-3-319-06483-3_8

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  • DOI: https://doi.org/10.1007/978-3-319-06483-3_8

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-06482-6

  • Online ISBN: 978-3-319-06483-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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