Issues in Negative Association Rule Mining with Business Analytics Perspectives

Jump To References Section


  • ,IN
  • ,IN


Association Rule Mining, Item sets, Negative Association Rules, Fuzzy Set Concept, Interestingness, Business Applications.
Strategic Management and Business Policy


Association Rule mining literature is witnessing a shift of focus from generating positive rules to the discovery of negative rules. A review of previous literature on negative rule mining that incorporate objective and subjective interestingness measures has been done. Then, an extension, to Fuzzy Set Concept for generating and mining negative rules is made. This work also presents unaddressed issues in mining of both positive and negative rules. Business applications that gain useful insights from both positive and negative rules have been highlighted.




How to Cite

Sethi, R., & Shekar, B. (2018). Issues in Negative Association Rule Mining with Business Analytics Perspectives. DHARANA - Bhavan’s International Journal of Business, 11(2), 13–20. Retrieved from



Research Articles



Agrawal, R., and Srikant, R. (1994, September). Fast algorithms for mining association rules. In Proc. 20th int. conf. very large data bases, VLDB (Vol. 1215, pp. 487-499).

Agrawal, R., Imieliski, T., and Swami, A. (1993). Mining association rules between sets of items in large databases. ACM SIGMOD Record, 22(2), 207-216.

Antonie, M. L., and Zaí¯ane, O. R. (2004). Mining positive and negative association rules: an approach for confined rules. In Knowledge Discovery in Databases: PKDD 2004 (pp. 27-38). Springer Berlin Heidelberg.

Apte, C., Liu, B., Pednault, E. P., and Smyth, P. (2002). Business applications of data mining. Communications of the ACM, 45(8), 49-53.

Brin, S., Motwani, R., and Silverstein, C. (1997a, June). Beyond market baskets: Generalizing association rules to correlations. In ACM SIGMOD Record (Vol. 26, No. 2, pp. 265-276). ACM.

Brin, S., Motwani, R., Ullman, J. D., and Tsur, S. (1997b, June). Dynamic itemset counting and implication rules for market basket data. In ACM SIGMOD Record (Vol. 26, No. 2, pp. 255-264). ACM.

Dong, G., and Li, J. (1998). Interestingness of discovered association rules in terms of neighborhood-based unexpectedness. In Research and Development in Knowledge Discovery and Data Mining (pp. 72-86). Springer Berlin Heidelberg.

Giudici, P. (2005). Applied data mining: statistical methods for business and industry. John Wiley & Sons.

Kuok, C. M., Fu, A., and Wong, M. H. (1998). Mining fuzzy association rules in databases. ACM Sigmod Record, 27(1), 41-46.

Mannila, H., Toivonen, H., and Verkamo, A. I. (1994, July). E cient algorithms for discovering association rules. In KDD-94: AAAI workshop on Knowledge Discovery in Databases (pp. 181-192).

Ngai, E. W. T., Hu, Y., Wong, Y. H., Chen, Y., and Sun, X. (2011). The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature. Decision Support Systems, 50(3), 559-569.

Padmanabhan, B., and Tuzhilin, A. (1999). Unexpectedness as a measure of interestingness in knowledge discovery. Decision Support Systems, 27(3), 303-318.

Savasere, A., Omiecinski, E., and Navathe, S. (1998, February). Mining for strong negative associations in a large database of customer transactions. In Data Engineering, 1998. Proceedings., 14th International Conference on (pp. 494-502). IEEE.

Shaw, G., Xu, Y., and Geva, S. (2009). Interestingness Measures for Multi-Level Association Rules. Proceedings of ADCS 2009, 27-34.

Srikant, R., and Agrawal, R. (1995). Mining generalized association rules (pp. 407-419). IBM Research Division.

Srikant, R., and Agrawal, R. (1996, June). Mining quantitative association rules in large relational tables. In Acm Sigmod Record (Vol. 25, No. 2, pp. 1-12). ACM.

Yuan, X., Buckles, B. P., Yuan, Z., and Zhang, J. (2002). Mining negative association rules. In Computers and Communications, 2002. Proceedings. ISCC 2002. Seventh International Symposium on (pp. 623-628). IEEE.