Abstract
The association rules, discovered by traditional support–confidence based algorithms, provide us with concise statements of potentially useful information hidden in databases. However, only considering the constraints of minimum support and minimum confidence is far from satisfying in many cases. In this paper, we propose a fuzzy method to formulate how interesting an association rule may be. It is indicated by the membership values belonging to two fuzzy sets (i.e., the stronger rule set and the weaker rule set), and thus provides much more flexibility than traditional methods to discover some potentially more interesting association rules. Furthermore, revised algorithms based on Apriori algorithm and matrix structure are designed under this framework.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Agrawal R, Imielinski T, Swami A (1993) Mining association rules between sets of items in large databases. In: Proceedings of the 1993 ACM SIGMOD conference on management of data, pp 207–216
Asuncion A, Newman DJ (2007) UCI Machine learning repository. School of Information and Computer Science, University of California, Irvine, CA. http://www.ics.uci.edu/~mlearn/MLRepository.html
Au WH, Chan KCC (2005) Mining changes in association rules: a fuzzy approach. Fuzzy Sets Syst 149:87–104
Brin S, Motwani R, Silverstein C (1997) Beyond market basket: generalizing association rules to correlations. In: Proceedings of the 1997 ACM SIGMOD international conference on management of data, pp 265–276
Coenen F, Goulbourne G, Leng P (2004) Tree structures for mining association rules. Data Min Knowl Discov 8:25–51
Dubois D, Prade H (1988) Fuzzy sets and systems. Academic Press, New York
Goulbourne G, Coenen F, Leng P (2000) Algorithms for computing association rules using a partial-support tree. Knowl Based Syst 13:141–149
Hastie T, Tibshirani R, Friedman J (2001) The elements of statistical learning: data mining, inference, and prediction. Springer, New York
Hong TP, Kuo CS, Chi SC (1999) Mining association rules from quantitative data. Intell Data Anal 3(5):363–376
Hu YC, Chen RS, Tzeng GH (2003) Discovering fuzzy association rules using fuzzy partition methods. Knowl Based Syst 16:137–147
Hussain F, Liu H, Suzuki E, Lu H (2000) Exception rule mining with a relative interestingness measure. Lect Notes Comput Sci 1805:86–97
Liu H, Lu H, Feng L, Hussain F (1999) Efficient search of reliable exceptions. Lect Notes Comput Sci 1574:194–204
Padmanabhan B, Tuzhilin A (1998) A belief-driven method for discovering unexpected patterns. In: Proceedings of the 4th international confercence on knowledge discovery and data mining
Padmanabhan B, Tuzhilin A (1999) Unexpectedness as a measure of interestingness in knowledge discovery. Decis Support Syst 27(3):303–318
Padmanabhan B, Tuzhilin A (2000) Small is beautiful: discovering the minimal set of unexpected patterns. In: Proceedings of the 6th ACM SIGKDD international conference on knowledge discovery and data mining, pp 54–63
Piatetsky-Shapiro G (1991) Discovery, analysis, and presentation of strong rules. In: Piatetsky-Shapiro G, Frawley WJ (eds) Knowledge discovery in databases. AAAI/MIT Press, Cambridge, pp 229–248
Srikant R, Agrawal R (1996) Mining quantitative association rules in large relational tables. In: Proceedings of the 1996 ACM SIGMOD international conference on management of data, pp 1–12
Wu X, Zhang C, Zhang S (2004) Efficient mining of both positive and negative association rule. ACM Trans Inf Syst 22(3):381–405
Xiong H, Tan PN, Kumar V (2003) Mining strong affinity association patterns in data sets with skewed support distribution. In: Proceedings of the third IEEE international conference on data mining (ICDM03), pp 387–394
Yuan X, Buckles B, Yuan Z, Zhang J (2002) Mining negative association rules. In: Proceedings of the seventh international symposium on computers and communications, pp 623–629
Yuan Y, Huang T (2005) A matrix algorithm for mining association rules. Lect Notes Comput Sci 3644:370–379
Yue S, Tsang E, Yeung D, Shi D (2000) Mining fuzzy association rules with weighted items. In: Proceedings of the IEEE international conference on systems, man and cybernetics, pp 1906–1911
Yun H, Ha D, Hwang B, Ryu KH (2003) Mining association rules on significant rare data using relative support. J Syst Softw 67:181–191
Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353
Zhou L, Yau S (2007) Efficient association rule mining among both frequent and infrequent items. Comput Math Appl 54:737–749
Acknowledgments
The authors would like to thank the anonymous reviewers for their constructive comments. These comments have contributed to a vast improvement of this paper. The work was partly supported by the National Natural Science Foundation of China (70671004), Program for New Century Excellent Talents in University (NCET-06-0172), a foundation for the author of the National Excellent Doctoral Dissertation of PR China (200782), and the Shuguang Plan of Shanghai Education Development Foundation and Shanghai Education Committee (08SG21).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Ma, WM., Wang, K. & Liu, ZP. Mining potentially more interesting association rules with fuzzy interest measure. Soft Comput 15, 1173–1182 (2011). https://doi.org/10.1007/s00500-010-0579-x
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-010-0579-x