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An Incremental Algorithm for Maintaining the Built FUSP Trees Based on the Pre-large Concepts

  • Chun-Wei Lin
  • Wensheng Gan
  • Tzung-Pei Hong
  • Raylin Tso
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 297)

Abstract

Mining useful information or knowledge from a very large database to aid managers or decision makers to make appropriate decisions is a critical issue in recent years. In this paper, we adopted the pre-large concepts to the FUSP-tree structure for sequence insertion. A FUSP tree is built in advance to keep the large 1-sequences for later maintenance. The pre-large sequences are also kept to reduce the movement from large to small and vice versa. When the number of inserted sequences is smaller than the safety bound of the pre-large concepts, better results can be obtained by the proposed incremental algorithm for sequence insertion in dynamic databases.

Keywords

Pre-large concept dynamic databases sequential pattern mining sequence insertion FUSP-tree structure 

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References

  1. 1.
    Agrawal, R., Imielinski, T., Swami, A.: Database mining: A performance perspective. IEEE Transactions on Knowledge and Data Engineering 5, 914–925 (2006)CrossRefGoogle Scholar
  2. 2.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: The International Conference on Very Large Data Bases, pp. 487–499 (1994)Google Scholar
  3. 3.
    Agrawal, R., Srikant, R.: Mining sequential patterns. In: The International Conference on Data Engineering, pp. 3–14 (1995)Google Scholar
  4. 4.
    Chen, M.S., Han, J., Philips Yu, S.: Data mining: An overview from a database perspective. IEEE Transactions on Knowledge and Data Engineering 8, 866–883 (1996)CrossRefGoogle Scholar
  5. 5.
    Wang, C.Y., Hong, T.P., Tseng, S.S.: Maintenance of sequential patterns for record deletion. In: IEEE International Conference on Data Mining, pp. 536–541 (2001)Google Scholar
  6. 6.
    Han, J., Pei, J., Yin, Y., Mao, R.: Mining frequent patterns without candidate generation: A frequent-pattern tree approach. Data Mining and Knowledge Discovery 8, 53–87 (2004)CrossRefMathSciNetGoogle Scholar
  7. 7.
    Hong, T.P., Wang, C.Y., Tao, Y.H.: A new incremental data mining algorithm using pre-large itemsets. Intelligent Data Analysis 5, 111–129 (2001)MATHGoogle Scholar
  8. 8.
    Hong, T.P., Lin, C.W., Wu, Y.L.: Incrementally fast updated frequent pattern trees. Expert Systems with Applications 34, 2424–2435 (2008)CrossRefGoogle Scholar
  9. 9.
    Hong, T.P., Wang, C.Y., Tseng, S.S.: An incremental mining algorithm for maintaining sequential patterns using pre-large sequences. Expert Systems with Applications 38, 7051–7058 (2011)CrossRefGoogle Scholar
  10. 10.
    Kim, C., Lim, J.H., Ng, R.T., Shim, K.: Squire: Sequential pattern mining with quantities. Journal of Systems and Software 80, 1726–1745 (2007)CrossRefGoogle Scholar
  11. 11.
    Lin, C.W., Hong, T.P., Lu, W.H., Lin, W.Y.: An incremental fusp-tree maintenance algorithm. In: The International Conference on Intelligent Systems Design and Applications, pp. 445–449 (2008)Google Scholar
  12. 12.
    Lin, C.W., Hong, T.P., Lu, W.H.: An efficient fusp-tree update algorithm for deleted data in customer sequences. In: International Conference on Innovative Computing, Information and Control, pp. 1491–1494 (2009)Google Scholar
  13. 13.
    Lin, C.W., Hong, T.P.: A survey of fuzzy web mining. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 3, 190–199 (2013)Google Scholar
  14. 14.
    Lin, M.Y., Lee, S.Y.: Incremental update on sequential patterns in large databases. In: IEEE International Conference on Tools with Artificial Intelligence, pp. 24–31 (1998)Google Scholar
  15. 15.
    Nakagaito, F., Ozaki, T., Ohkawa, T.: Discovery of quantitative sequential patterns from event sequences. In: IEEE International Conference on Data Mining Workshops, pp. 31–36 (2009)Google Scholar
  16. 16.
    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 Transactions on Knowledge and Data Engineering 16, 1424–1440 (2004)CrossRefGoogle Scholar
  17. 17.
    Ren, J.M., Jang, J.R.: Discovering time-constrained sequential patterns for music genre classification. IEEE Transactions on Audio, Speech, and Language Processing 20, 1134–1144 (2012)CrossRefGoogle Scholar
  18. 18.
    Zheng, Z., Kohavi, R., Mason, L.: Real world performance of association rule algorithms. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 401–406 (2001)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Chun-Wei Lin
    • 1
    • 2
  • Wensheng Gan
    • 1
  • Tzung-Pei Hong
    • 3
    • 4
  • Raylin Tso
    • 5
  1. 1.Innovative Information Industry Research Center (IIIRC)ShenzhenP.R. China
  2. 2.Shenzhen Key Laboratory of Internet Information Collaboration, School of Computer Science and TechnologyHarbin Institute of Technology Shenzhen Graduate SchoolShenzhenP.R. China
  3. 3.Department of Computer Science and Information EngineeringNational University of KaohsiungKaohsiungTaiwan, R.O.C.
  4. 4.Department of Computer Science and EngineeringNational Sun Yat-sen UniversityKaohsiungTaiwan, R.O.C.
  5. 5.Department of Computer ScienceNational Chengchi UniversityTaipeiTaiwan, R.O.C.

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