, 93:1 | Cite as

Query-based association rule mining supporting user perspective

  • Seung-Jae Song
  • Eung-Hee Kim
  • Hong-Gee Kim
  • Harshit Kumar


Two parameters, namely support and confidence, in association rule mining, are used to arrange association rules in either increasing or decreasing order. These two parameters are assigned values by counting the number of transactions satisfying the rule without considering user perspective. Hence, an association rule, with low values of support and confidence, but meaningful to the user, does not receive the same importance as is perceived by the user. Reflecting user perspective is of paramount importance in light of improving user satisfaction for a given recommendation system. In this paper, we propose a model and an algorithm to extract association rules, meaningful to a user, with an ad-hoc support and confidence by allowing the user to specify the importance of each transaction. In addition, we apply the characteristics of a concept lattice, a core data structure of Formal Concept Analysis (FCA) to reflect subsumption relation of association rules when assigning the priority to each rule. Finally, we describe experiment results to verify the potential and efficiency of the proposed method.


Data mining Association rule Formal concept analysis User perspective 

Mathematics Subject Classification (2000)



  1. 1.
    Shmueli G, Patel NR, Bruce PC (2006) Data mining for business intelligence. Wiley, LondonGoogle Scholar
  2. 2.
    Tan P-N, Steinbach M, Kumar V (2005) Introduction to data mining. Addison Wesley, USAGoogle Scholar
  3. 3.
    Agrawal R, Imielinski T, Swami A (1993) Issue mining association rules between sets of items in large databases. SIGMOD Rec. 22(2): 207–216CrossRefGoogle Scholar
  4. 4.
    Brin S, Motwani R, Silverstein C (1997) Beyond market baskets: generalizing association rules to correlations. SIGMOD Rec. 26(2): 265–276CrossRefGoogle Scholar
  5. 5.
    Schmitz C, Hotho A, Jaschke R, Stumme G (2006) Mining association rules in Folksonomies. In: Batagelj V, Bock H-H, Ferligoj A, Ziberna A (eds) Data science and classification: Proc. of the 10th IFCS Conf. Studies in classification, data analysis, and knowledge organization. Springer, Berlin, Heidelberg, pp 261–270Google Scholar
  6. 6.
    Srikant R, Agrawal R (1996) Mining quantitative association rules in large relational tables. SIGMOD Rec. 25(2): 1–12CrossRefGoogle Scholar
  7. 7.
    Aumann Y, Lindell Y (2003) A statistical theory for quantitative association rules. J Intell Inf Syst 20(3): 255–283CrossRefGoogle Scholar
  8. 8.
    Shaw G, Xu Y, Geva S (2010) Using association rules to solve the cold-start problem in recommender systems. In: Zaki M (eds) Advances in knowledge discovery and data mining. Springer, Berlin, Heidelberg, pp 340–347CrossRefGoogle Scholar
  9. 9.
    Schein AI, Popescul A, Ungar LH, Pennock DM (2002) Methods and metrics for cold-start recommendations. Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval, pp 253–260Google Scholar
  10. 10.
    Weng L-T, Xu Y, Li Y, Nayak R (2008) Exploiting item Taxonomy for Solving cold-start problem in recommendation making. Proceedings of the 2008 20th IEEE International Conference on Tools with Artificial Intelligence, vol 2, pp 113–120Google Scholar
  11. 11.
    Ganter B, Stumme G, Wille R (2005) Formal concept analysis: foundations and applications. In: Jaime G, Jorg S (eds) Lecture notes in artificial intelligence. Springer, BerlinGoogle Scholar
  12. 12.
    Ganter B, Wille R (1998) Formal concept analysis: mathematical foundations. Springer, BerlinGoogle Scholar
  13. 13.
    Zhang S, Lu J, Zhang C (2004) A fuzzy logic based method to acquire user threshold of minimum-support for mining association rules. Inf Sci Inf Comput Sci 164: 1–16MATHGoogle Scholar
  14. 14.
    Pasquier N, Bastide Y, Taouiln R, Lakhal L (1999) Efficient mining of association rules using closed itemset lattices. Inf Syst 24(1): 25–46CrossRefGoogle Scholar
  15. 15.
    Kim E-H (2009) Formal concept analysis based association rule extraction and its applications, in School of Dentistry. Seoul National University, Korea, p 77Google Scholar
  16. 16.
    Yun H, Ha D, Hwang B, Ryu KH (2003) Mining association rules on significant rare data using relative support. J Syst Softw 67(3): 181–191CrossRefGoogle Scholar
  17. 17.
    Liu B, Hsu W, Ma Y (1999) Mining association rules with multiple minimum supports. Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining, ACM: San Diego, pp 337–341Google Scholar
  18. 18.
    Lakhal L, Stumme G (2005) Efficient mining of association rules based on formal concept analysis, in formal concept analysis. In: Ganter B, Stumme G, Wille R (eds) Lecture notes in computer science, vol 3626. Springer, Berlin, Heidelberg, pp 180–195Google Scholar
  19. 19.
    Hu K, Lu Y, Zhou L, Shi C (2004) Classification and association rule mining: a concept lattice framework, new directions in rough sets. Data Min Granul Soft Comput 1711: 443–447Google Scholar
  20. 20.
    Stumme G, Taouil R, Bastide Y, Pasquier N, Lakhal L (2001) Intelligent structuring and reducing of association rules with formal concept analysis. Advances in artificial intelligence, vol 2174. Lecture Notes in Computer Science, pp 335–350Google Scholar
  21. 21.
    Srikant R, Vu Q, Agrawal R (1996) Mining association rules with item constraints, SIGMOD International Conference on Management of Data. Montreal, Canada, pp 1–12Google Scholar
  22. 22.
    Agrawal R, Srikant R (1994) Fast algorithms for mining association rules. Proceedings of the 20th international conference on very large data bases, pp 487–499Google Scholar
  23. 23.
    Shepitsen A, Gemmell J, Mobasher B, Robin B (2008) Personalized recommendation in social tagging systems using hierarchical clustering, ACM conference on Recommender systems, pp 259–266Google Scholar
  24. 24.
    Ardissono L, Gena C, Torasso P, Bellifemine F, Chiarotto A, Difino A, Negro B (2003) Personalized recommendation of TV programs. AI*IA 2003: Advances in artificial intelligence, vol 2829. Springer-Verlag, pp 474–486Google Scholar
  25. 25.
    Kim JK, Cho YH, Kim WJ, Kim JR, Suh JY(2002) A personalized recommendation procedure for Internet shopping support. Electronic commerce research and applications, vol 1, pp 301–313Google Scholar
  26. 26.
    Bodik P, Fox A, Jordan MI, Patterson D, Banerjee A, Jagannathan R, Su T, Tenginakai S, Turner B, Ingalls J (2006) Advanced tools for operators at Hot topics in autonomic computing, pp 301–313Google Scholar
  27. 27.
    Changchien SW, Lu T-C (2001) Mining association rules procedure to support on-line recommendation by customers and products fragmentation. Expert Syst Appl 20: 325–335CrossRefGoogle Scholar
  28. 28.
    Wei J, Bressan S, Ooi BC (2000) Term association rules for automatic global query expansion: methodology and preliminary results. Proc First Int Conf Web Inf Syst Eng 1: 366–373CrossRefGoogle Scholar
  29. 29.
    Stumme G, Taouil R, Bastide Y, Pasquier N, Lakhal L (2002) Computing iceberg concept lattices with TITANIC. Data Knowl Eng 42: 189–222MATHCrossRefGoogle Scholar
  30. 30.
    Marek L (2008) Tag recommendation for folksonomies oriented towards individual users. In proceedings of the ECML/PKDD 2008 Discovery Challenge WorkshopGoogle Scholar

Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  • Seung-Jae Song
    • 1
  • Eung-Hee Kim
    • 1
  • Hong-Gee Kim
    • 1
  • Harshit Kumar
    • 1
    • 2
  1. 1.Biomedical Knowledge Engineering Lab, Dental Research InstituteSeoul National UniversitySeoulKorea
  2. 2.Department of Computer ScienceUniversity of SuwonHwaseongKorea

Personalised recommendations