Call for Paper - January 2023 Edition
IJCA solicits original research papers for the January 2023 Edition. Last date of manuscript submission is December 20, 2022. Read More

An Efficient Approach for Extraction of Actionable Association Rules

International Journal of Computer Applications
© 2012 by IJCA Journal
Volume 54 - Number 11
Year of Publication: 2012
Prashasti Kanikar
Ketan Shah

Prashasti Kanikar and Ketan Shah. Article: An Efficient Approach for Extraction of Actionable Association Rules. International Journal of Computer Applications 54(11):5-10, September 2012. Full text available. BibTeX

	author = {Prashasti Kanikar and Ketan Shah},
	title = {Article: An Efficient Approach for Extraction of Actionable Association Rules},
	journal = {International Journal of Computer Applications},
	year = {2012},
	volume = {54},
	number = {11},
	pages = {5-10},
	month = {September},
	note = {Full text available}


Traditional association mining often produces large numbers of association rules and sometimes it is very difficult for users to understand such rules and apply this knowledge to any business process. So, to find actionable knowledge from resultant association rules, the idea of combined patterns is explored in this paper. Combined Mining is a kind of post processing method for extracting actionable association rules from all possible association rules generated using any algorithm like Apriori or FP tree. In this approach, first the association rules are filtered by varying support and confidence levels, then using the interestingness measure Irule , it is decided whether it is useful to combine the association rules or individual rules are more powerful. For experimental purpose, the Combined Mining approach is applied on a survey dataset and the results prove that the method is very efficient than the traditional mining approach for obtaining actionable rules. The scheme of combined association rule mining can be extended for combined rule pairs and combined rule clusters. The efficiency can be further improved by the parallel implementation of this approach.


  • Y. Zhao, H. Zhang, L. Cao, C. Zhang, and H. Bohlscheid, "Combined pattern mining: From learned rules to actionable knowledge," in Proc. AI, 2008, pp. 393–403.
  • Longbing Cao, Huaifeng Zhang,Yanchang Zhao, Dan Luo and Chengqi Zhang, "Combined Mining: Discovering Informative Knowledge in Complex Data", IEEE Transactions on Systems, Man and Cybernetics—part B: CYBERNETICS, vol. 41, no. 3, june 2011, pp. 699-712.
  • Za¨?ane, O. R. , Antonie, M. -L. " On pruning and tuning rules for associative classifiers", KES 2005. LNCS (LNAI), vol. 3683, pp. 966–973.
  • Zhiwen Yu, Xing Wang ,Hau-San Wong and Zhongkai Deng,"Pattern mining based on local distribution", IEEE,2008,pp. 584-588.
  • Shigeaki Sakurai, Youichi Kitahara, and Ryohei Orihara," Sequential Pattern Mining based on a New Criteria and Attribute Constraints", IEEE, 2007,pp. 516-521.
  • Unil Yun, and John J. Leggett," WSpan: Weighted Sequential pattern mining in large sequence databases", 3rd International IEEE Conference Intelligent Systems, September 2006, pp. 512-517.
  • P. S. Wang, "Survey on Privacy Preserving Data Mining", JDCTA: Journal of Digital Content Technology and its Applications, Vol. 4, No. 9, pp. 1 -7, 2010.
  • Lingjuan Li, Min Zhang , "The Strategy of Mining Association Rule Based on Cloud Computing" , International Conference on Business Computing and Global Informatization , 2011,pp. 475-478.
  • Ashish Mangalampalli, Supervised by Vikram Pudi," Fuzzy Associative Rule-based Approach for Pattern Mining and Identification and Pattern-based Classification", WWW 2011, March 28–April 1, 2011, Hyderabad, India,pp. 379-383.
  • T. Brijs, K. Vanhoof, G. Wets," Defining Interestingness for Association Rules", International Journal "Information Theories & Applications, Vol. 10,pp. 370-375.
  • http://www. rsscse. org. uk/stats4schools
  • Goulbourne, G. , Coenen, F. and Leng, P. (2000), "Algorithms for Computing Association Rules Using a Partial-Support Tree", Journal of Knowledge-Based Systems, Vol (13), pp141-149.
  • Coenen, F. , Goulbourne, G. and Leng, P. , (2003). "Tree Structures for Mining association Rules", Journal of Data Mining and Knowledge Discovery, Vol 8, No 1, pp25-51.
  • W. J. Frawley, G. Piatetsky-Shapiro & C. J. Matheus ,"Knowledge discovery in databases: an overview", Knowledge Discovery in Databases (1991), pp1-27.
  • U. M. Fayyad, G. Piatetsky-Shapiro & P. Smyth, "From data mining to knowledge discovery", Advances in Knowledge Discovery and Data Mining (1996), pp. 1-34.
  • Agrawal, Rakesh; Imielinski, Tomasz; Swami, Arun," Mining Association Rules Between Sets of Items in Large Databases", SIGMOD Conference 1993,pp. 207-216
  • Prashasti Kanikar, Dr. Ketan Shah, "Extracting Actionable Association Rules from Multiple Datasets", International Journal of Engineering Research and Applications (IJERA), May 2012.