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Sequential Pattern Mining

  • Wei ShenEmail author
  • Jianyong Wang
  • Jiawei Han
Chapter

Abstract

Sequential pattern mining, which discovers frequent subsequences as patterns in a sequence database, has been a focused theme in data mining research for over a decade. This problem has broad applications, such as mining customer purchase patterns and Web access patterns. However, it is also a challenging problem since the mining may have to generate or examine a combinatorially explosive number of intermediate subsequences. Abundant literature has been dedicated to this research and tremendous progress has been made so far. This chapter will present a thorough overview and analysis of the main approaches to sequential pattern mining.

Keywords

Sequential pattern mining 

References

  1. 1.
    J. Wang, “Sequential patterns,” Encyclopedia of Database Systems. LING LIU and M. TAMER OZSU (Eds.), pp. 2621–2626, 2009.Google Scholar
  2. 2.
    J. Pei, J. Han, and W. Wang, “Constraint-based sequential pattern mining: the pattern-growth methods,” J. Intell. Inf. Syst., vol. 28, no. 2, pp. 133–160, Apr. 2007.CrossRefGoogle Scholar
  3. 3.
    J. Han, J. Pei, and X. Yan, “Sequential pattern mining by pattern-growth: Principles and extensions,” StudFuzz, vol. 180, pp. 183–220, 2005.Google Scholar
  4. 4.
    J. Han, H. Cheng, D. Xin, and X. Yan, “Frequent pattern mining: current status and future directions,” Data Min. Knowl. Discov., vol. 15, no. 1, pp. 55–86, Aug. 2007.CrossRefMathSciNetGoogle Scholar
  5. 5.
    N. R. Mabroukeh and C. I. Ezeife, “A taxonomy of sequential pattern mining algorithms,” ACM Comput. Surv., vol. 43, no. 1, pp. 3:1–3:41, Dec. 2010.CrossRefGoogle Scholar
  6. 6.
    C. H. Mooney and J. F. Roddick, “Sequential pattern mining—approaches and algorithms,” ACM Comput. Surv., vol. 45, no. 2, pp. 19:1–19:39, Mar. 2013.CrossRefGoogle Scholar
  7. 7.
    R. Agrawal, T. Imieliński, and A. Swami, “Mining association rules between sets of items in large databases,” in ACM SIGMOD conference, 1993, pp. 207–216.Google Scholar
  8. 8.
    R. Agrawal and R. Srikant, “Mining sequential patterns,” in ICDE Conference, 1995, pp. 3–14.Google Scholar
  9. 9.
    R. Srikant and R. Agrawal, “Mining sequential patterns: Generalizations and performance improvements,” in EDBT Conference, 1996, pp. 3–17.Google Scholar
  10. 10.
    F. Masseglia, F. Cathala, and P. Poncelet, “The psp approach for mining sequential patterns,” in PKDD Conference, 1998, pp. 176–184.Google Scholar
  11. 11.
    M. J. Zaki, “Spade: An efficient algorithm for mining frequent sequences,” Mach. Learn., vol. 42, no. 1–2, pp. 31–60, Jan. 2001.CrossRefzbMATHGoogle Scholar
  12. 12.
    J. Ayres, J. Flannick, J. Gehrke, and T. Yiu, “Sequential pattern mining using a bitmap representation,” in ACM SIGKDD Conference, 2002, pp. 429–435.Google Scholar
  13. 13.
    L. Savary and K. Zeitouni, “Indexed bit map (ibm) for mining frequent sequences,” in PKDD Conference, 2005, pp. 659–666.Google Scholar
  14. 14.
    Z. Yang and M. Kitsuregawa, “Lapin-spam: An improved algorithm for mining sequential pattern,” in ICDE Workshops, 2005.Google Scholar
  15. 15.
    J. Han, J. Pei, B. Mortazavi-Asl, Q. Chen, U. Dayal, and M.-C. Hsu, “Freespan: frequent pattern-projected sequential pattern mining,” in ACM SIGKDD Conference, 2000, pp. 355–359.Google Scholar
  16. 16.
    J. Pei, J. Han, B. Mortazavi-asl, H. Pinto, Q. Chen, U. Dayal, and M. chun Hsu, “Prefixspan: Mining sequential patterns efficiently by prefix-projected pattern growth,” in ICDE Conference, 2001, pp. 215–224.Google Scholar
  17. 17.
    J. Han and J. Pei, “Mining frequent patterns by pattern-growth: methodology and implications,” SIGKDD Explor. Newsl., vol. 2, no. 2, pp. 14–20, Dec. 2000.CrossRefGoogle Scholar
  18. 18.
    C. Raïssi and J. Pei, “Towards bounding sequential patterns,” in ACM SIGKDD, 2011, pp. 1379–1387.Google Scholar
  19. 19.
    R. Agrawal and R. Srikant, “Fast algorithms for mining association rules in large databases,” in VLDB Conference, 1994, pp. 487–499.Google Scholar
  20. 20.
    B. A. Davey and H. A. Priestley, Eds., Introduction to lattices and order. Cambridge: Cambridge University Press, 1990.zbMATHGoogle Scholar
  21. 21.
    M. J. Zaki and C. jui Hsiao, “Charm: An efficient algorithm for closed itemset mining,” in SDM Conference, 2002, pp. 457–473.Google Scholar
  22. 22.
    J. Pei, J. Han, and R. Mao, “Closet: An efficient algorithm for mining frequent closed itemsets,” in ACM SIGMOD Workshop, 2000, pp. 21–30.Google Scholar
  23. 23.
    J. Wang, J. Han, and J. Pei, “Closet+: searching for the best strategies for mining frequent closed itemsets,” in ACM SIGKDD Conference, 2003, pp. 236–245.Google Scholar
  24. 24.
    F. Pan, G. Cong, A. K. H. Tung, J. Yang, and M. J. Zaki, “Carpenter: finding closed patterns in long biological datasets,” in ACM SIGKDD Conference, 2003, pp. 637–642.Google Scholar
  25. 25.
    X. Yan, J. Han, and R. Afshar, “Clospan: Mining closed sequential patterns in large datasets,” in SDM Conference, 2003, pp. 166–177.Google Scholar
  26. 26.
    J. Wang and J. Han, “Bide: Efficient mining of frequent closed sequences,” in ICDE Conference, 2004.Google Scholar
  27. 27.
    J. Wang, J. Han, and C. Li, “Frequent closed sequence mining without candidate maintenance,” TKDE, vol. 19, no. 8, pp. 1042–1056, 2007.Google Scholar
  28. 28.
    C. Li, Q. Yang, J. Wang, and M. Li, “Efficient mining of gap-constrained subsequences and its various applications,” ACM Trans. Knowl. Discov. Data, vol. 6, no. 1, pp. 2:1–2:39, Mar. 2012.CrossRefMathSciNetGoogle Scholar
  29. 29.
    J. Wang, Y. Zhang, L. Zhou, G. Karypis, and C. C. Aggarwal, “Contour: An efficient algorithm for discovering discriminating subsequences,” Data Min. Knowl. Discov., vol. 18, no. 1, pp. 1–29, Feb. 2009.CrossRefMathSciNetGoogle Scholar
  30. 30.
    H. Pinto, J. Han, J. Pei, K. Wang, Q. Chen, and U. Dayal, “Multi-dimensional sequential pattern mining,” in CIKM Conference, 2001, pp. 81–88.Google Scholar
  31. 31.
    C.-C. Yu and Y.-L. Chen, “Mining sequential patterns from multidimensional sequence data,” IEEE Trans. on Knowl. and Data Eng., vol. 17, no. 1, pp. 136–140, Jan. 2005.CrossRefGoogle Scholar
  32. 32.
    S. Parthasarathy, M. J. Zaki, M. Ogihara, and S. Dwarkadas, “Incremental and interactive sequence mining,” in CIKM Conference, 1999, pp. 251–258.Google Scholar
  33. 33.
    F. Masseglia, P. Poncelet, and M. Teisseire, “Incremental mining of sequential patterns in large databases,” Data Knowl. Eng., vol. 46, no. 1, pp. 97–121, 2003.CrossRefGoogle Scholar
  34. 34.
    H. Cheng, X. Yan, and J. Han, “Incspan: incremental mining of sequential patterns in large database,” in ACM SIGKDD Conference, 2004, pp. 527–532.Google Scholar
  35. 35.
    C. Gao, J. Wang, and Q. Yang, “Efficient mining of closed sequential patterns on stream sliding window,” in ICDM Conference, 2011.Google Scholar
  36. 36.
    D.-Y. Chiu, Y.-H. Wu, and A. L. P. Chen, “An efficient algorithm for mining frequent sequences by a new strategy without support counting,” in ICDE Conference, 2004, pp. 375–386.Google Scholar
  37. 37.
    H. Kum, J. Pei, W. Wang, and D. Duncan, “Approxmap: Approximate mining of consensus sequential patterns,” in SDM Conference, 2002.Google Scholar
  38. 38.
    P. Tzvetkov, X. Yan, and J. Han, “Tsp: Mining top-k closed sequential patterns,” in ICDM Conference, 2003.Google Scholar
  39. 39.
    H. Mannila, H. Toivonen, and A. I. Verkamo, “Discovering frequent episodes in sequences,” in KDD Conference, 1995, pp. 210–215.Google Scholar
  40. 40.
    N. Tatti and B. Cule, “Mining closed strict episodes,” in ICDM Conference, 2010, pp. 501–510.Google Scholar
  41. 41.
    N. Tatti and B. Cule, “Mining closed strict episodes,” Data Min. Knowl. Discov., vol. 25, no. 1, pp. 34–66, Jul. 2012.Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Tsinghua UniversityBeijingChina
  2. 2.University of Illinois at Urbana-ChampaignUrbanaIllinois

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