Skip to main content

Advertisement

SpringerLink
Log in

Query-based association rule mining supporting user perspective

  • Published:
Computing Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Shmueli G, Patel NR, Bruce PC (2006) Data mining for business intelligence. Wiley, London

    Google Scholar 

  2. Tan P-N, Steinbach M, Kumar V (2005) Introduction to data mining. Addison Wesley, USA

    Google Scholar 

  3. Agrawal R, Imielinski T, Swami A (1993) Issue mining association rules between sets of items in large databases. SIGMOD Rec. 22(2): 207–216

    Article  Google Scholar 

  4. Brin S, Motwani R, Silverstein C (1997) Beyond market baskets: generalizing association rules to correlations. SIGMOD Rec. 26(2): 265–276

    Article  Google Scholar 

  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 261–270

    Google Scholar 

  6. Srikant R, Agrawal R (1996) Mining quantitative association rules in large relational tables. SIGMOD Rec. 25(2): 1–12

    Article  Google Scholar 

  7. Aumann Y, Lindell Y (2003) A statistical theory for quantitative association rules. J Intell Inf Syst 20(3): 255–283

    Article  Google Scholar 

  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 340–347

    Chapter  Google Scholar 

  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 253–260

  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 113–120

  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, Berlin

    Google Scholar 

  12. Ganter B, Wille R (1998) Formal concept analysis: mathematical foundations. Springer, Berlin

    Google Scholar 

  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: 1–16

    MATH  Google Scholar 

  14. Pasquier N, Bastide Y, Taouiln R, Lakhal L (1999) Efficient mining of association rules using closed itemset lattices. Inf Syst 24(1): 25–46

    Article  Google Scholar 

  15. Kim E-H (2009) Formal concept analysis based association rule extraction and its applications, in School of Dentistry. Seoul National University, Korea, p 77

    Google Scholar 

  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): 181–191

    Article  Google Scholar 

  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 337–341

  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 180–195

    Google Scholar 

  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: 443–447

    Google Scholar 

  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 335–350

  21. Srikant R, Vu Q, Agrawal R (1996) Mining association rules with item constraints, SIGMOD International Conference on Management of Data. Montreal, Canada, pp 1–12

  22. Agrawal R, Srikant R (1994) Fast algorithms for mining association rules. Proceedings of the 20th international conference on very large data bases, pp 487–499

  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 259–266

  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 474–486

  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 301–313

  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 Amazon.com. Hot topics in autonomic computing, pp 301–313

  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: 325–335

    Article  Google Scholar 

  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: 366–373

    Article  Google Scholar 

  29. Stumme G, Taouil R, Bastide Y, Pasquier N, Lakhal L (2002) Computing iceberg concept lattices with TITANIC. Data Knowl Eng 42: 189–222

    Article  MATH  Google Scholar 

  30. Marek L (2008) Tag recommendation for folksonomies oriented towards individual users. In proceedings of the ECML/PKDD 2008 Discovery Challenge Workshop

Download references

Author information

Authors and Affiliations

  1. Biomedical Knowledge Engineering Lab, Dental Research Institute, Seoul National University, Seoul, Korea

    Seung-Jae Song, Eung-Hee Kim, Hong-Gee Kim & Harshit Kumar

  2. Department of Computer Science, University of Suwon, Hwaseong, Korea

    Harshit Kumar

Authors
  1. Seung-Jae Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Eung-Hee Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hong-Gee Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Harshit Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hong-Gee Kim.

Additional information

This work was supported by the Information Technology Research and Development program of the Ministry of Knowledge Economy, Korea and Korea Evaluation Institute of Industrial Technology. [10033187, Integrated Frame Technology Development for Digital Hospital Information Systems and Medical Equipments].

Rights and permissions

Reprints and permissions

About this article

Cite this article

Song, SJ., Kim, EH., Kim, HG. et al. Query-based association rule mining supporting user perspective. Computing 93, 1–25 (2011). https://doi.org/10.1007/s00607-011-0148-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00607-011-0148-x

Keywords

Mathematics Subject Classification (2000)

Search

Navigation