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Knowledge and Information Systems

, Volume 23, Issue 1, pp 73–98 | Cite as

Mining dynamic association rules with comments

  • Bin Shen
  • Min Yao
  • Zhaohui Wu
  • Yunjun Gao
Regular Paper

Abstract

In this paper, we study a new problem of mining dynamic association rules with comments (DAR-C for short). A DAR-C contains not only rule itself, but also its comments that specify when to apply the rule. In order to formalize this problem, we first present the expression method of candidate effective time slots, and then propose several definitions concerning DAR-C. Subsequently, two algorithms, namely ITS2 and EFP-Growth2, are developed for handling the problem of mining DAR-C. In particular, ITS2 is an improved two-stage dynamic association rule mining algorithm, while EFP-Growth2 is based on the EFP-tree structure and is suitable for mining high-density mass data. Extensive experimental results demonstrate that the efficiency and scalability of our proposed two algorithms (i.e., ITS2 and EFP-Growth2) on DAR-C mining tasks, and their practicability on real retail dataset.

Keywords

Dynamic association rule Comment Support vector Confidence vector Mining algorithm 

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References

  1. 1.
    Agrawal R, Imielinski T, Swami A (1993) Mining association rules between sets of items in large databases. In: Proceedings of the ACM SIGMOD conference on management of data, pp 207–216Google Scholar
  2. 2.
    Agrawal R, Srikant R (1994) Fast algorithms for mining association rules. In: Proceedings of the 20th international conference on very large data bases, pp 487–499Google Scholar
  3. 3.
    Cheung DW, Han J, Ng VT, Wong CY (1996) Maintenance of discovered association rules in large databases: an incremental updating technique. In: Proceedings of the 12th international conference on data engineering, pp 106–114Google Scholar
  4. 4.
    Cheng J, Ke Y, Ng W (2008) A survey on algorithms for mining frequent itemsets over data streams. Knowl Inf Syst 16(1): 1–27CrossRefMathSciNetGoogle Scholar
  5. 5.
    Zhang S, Huang Z, Zhang J, Zhu X (2008) Mining follow-up correlation patterns from time-related database. Knowl Inf Syst 14(1): 81–100CrossRefGoogle Scholar
  6. 6.
    Ke Y, Cheng J, Ng W (2008) An information-theoretic approach to quantitative association rule mining. Knowl Inf Syst 16(2): 213–244CrossRefMathSciNetGoogle Scholar
  7. 7.
    Liu J, Rong G (2005) Mining dynamic association rules in databases. In: Proceedings of the international conference on computational intelligence and security, pp 688–695Google Scholar
  8. 8.
    Agrawal R, Srikant R (1995) Mining sequential patterns. In: Proceedings of the 11th international conference on data engineering, pp 3–14Google Scholar
  9. 9.
    Han J, Pei J, Mortazavi-Asl B, Chen Q, Dayal U, Hsu M (2000) FreeSpan: frequent pattern-projected sequential pattern mining. In: Proceedings of the sixth ACM SIGKDD international conference on knowledge discovery and data mining, pp 355–359Google Scholar
  10. 10.
    Garofalakis MN, Rastogi R, Shim K (1999) SPIRIT: sequential pattern mining with regular expression constraints. In: Proceedings of the 25th international conference on very large data bases, pp 223–234Google Scholar
  11. 11.
    Srikant R, Agrawal R (1996) Mining sequential patterns: generalizations and performance improvements. In: Proceedings of the 5th international conference on extending database technology, pp 3–17Google Scholar
  12. 12.
    Ozden B, Ramaswamy S, Silberschatz A (1998) Cyclic association rules. In: Proceedings of the 14th international conference on data engineering, pp 412–421Google Scholar
  13. 13.
    Qin M, Hwang K (2004) Frequent episode rules for Internet anomaly detection. In: Proceedings of the 3rd IEEE international symposium on network computing and applications, pp 161–168Google Scholar
  14. 14.
    Han J, Gong W, Yin Y (1998) Mining segment-wise periodic patterns in time-related databases. In: Proceedings of the 4th international conference on knowledge discovery and data mining, pp 214–218Google Scholar
  15. 15.
    Verma K, Vyas OP (2005) Efficient calendar based temporal association rule. SIGMOD Record 34(3): 63–70CrossRefGoogle Scholar
  16. 16.
    Li Y, Ning P, Sean Wang X, Jajodia S (2003) Discovering calendar-based temporal association rules. Data Knowl Eng 44(2): 193–218CrossRefGoogle Scholar
  17. 17.
    Lu H, Han J, Feng L (1998) Stock movement prediction and n-dimensional inter-transaction association rules. In: Proceedings of the 3rd ACM-SIGMOD workshop on research issues on data mining and knowledge discovery, vol~12, pp 1–7Google Scholar
  18. 18.
    Agrawal R, Psaila G (1995) Active data mining. In: Proceedings of the 1st international conference on knowledge discovery and data mining, pp 3–8Google Scholar
  19. 19.
    Ganti V, Gehrke J, Ramakrishnan R (1999) A framework for measuring changes in data characteristics. In: Proceedings of the 18th ACM SIGMOD-SIGACT-SIGART symposium on principles of database systems, pp 126–137Google Scholar
  20. 20.
    Liu B, Hsu W, Ma Y (2001) Discovering the set of fundamental rule changes. In: Proceedings of the 7th ACM SIGKDD international conference on knowledge discovery and data mining, pp 335–340Google Scholar
  21. 21.
    Dong G, Li J (2005) Mining border descriptions of emerging patterns from dataset pairs. Knowl Inf Syst 8(2): 178–202CrossRefGoogle Scholar
  22. 22.
    Au W-H, Chan KCC (2002) Fuzzy data mining for discovering changes in association rules overtime. In: Proceedings of the 2002 IEEE international conference on fuzzy systems, vol 2, pp 890–895Google Scholar
  23. 23.
    Au W-H, Chan KCC (2005) Mining changes in association rules: a fuzzy approach. Fuzzy Sets Syst 149(1): 87–104zbMATHCrossRefMathSciNetGoogle Scholar
  24. 24.
    Liu B, Hsu W, Han H-S, Xia Y (2000) Mining changes for real-life applications. In: Proceedings of the 2nd international conference on data warehousing and knowledge discovery, pp 337–346Google Scholar
  25. 25.
    Shen B, Yao M (2007) Research on a new kind of dynamic association rule and its mining algorithms. http://www.paper.edu.cn/paper.php?serial_number=200712-3
  26. 26.
    Ramaswamy S, Mahajan S, Silberschatz A (1998) On the discovery of interesting patterns in association rules. In: Proceedings of the 24th international conference on very large data bases, pp 368–379Google Scholar
  27. 27.
    Lee W-J, Jiang J-Y, Lee S-J (2008) Mining fuzzy periodic association rules. Data Knowl Eng 65(3): 442–462CrossRefGoogle Scholar
  28. 28.
    Burdick D, Calimlim M, Gehrke J (2001) MAFIA: a maximal frequent itemset algorithm for transactional databases. In: Proceedings of the 17th international conference on data engineering, pp 443–452Google Scholar
  29. 29.
    Han J, Pei J, Yin Y, Mao R (2004) Mining frequent patterns without candidate generation: a frequent-pattern tree approach. Data Min Knowl Discov 8(1): 53–87CrossRefMathSciNetGoogle Scholar
  30. 30.
    IBM Almaden Research Center (2009) Quest synthetic data generation code. http://www.almaden.ibm.com/cs/projects/iis/hdb/Projects/data_mining/datasets/syndata.html
  31. 31.
    Deepa Sheenoy P, Srinivasa KG, Venugopal KR, Patnaik LM (2005) Dynamic association rule mining using genetic algorithms. Intell Data Anal 9(5): 439–453Google Scholar

Copyright information

© Springer-Verlag London Limited 2009

Authors and Affiliations

  1. 1.College of Computer Science and TechnologyZhejiang UniversityHangzhouChina
  2. 2.Ningbo Institute of TechnologyZhejiang University NingboChina
  3. 3.School of Information SystemsSingapore Management UniversitySingaporeSingapore

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