Call for Paper - August 2019 Edition
IJCA solicits original research papers for the August 2019 Edition. Last date of manuscript submission is July 20, 2019. Read More

Optimizing Association Rule using Genetic Algorithm and Data Sampling Approach

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2018
Devyani Ojha, Pragya Pandey

Devyani Ojha and Pragya Pandey. Optimizing Association Rule using Genetic Algorithm and Data Sampling Approach. International Journal of Computer Applications 179(11):15-19, January 2018. BibTeX

	author = {Devyani Ojha and Pragya Pandey},
	title = {Optimizing Association Rule using Genetic Algorithm and Data Sampling Approach},
	journal = {International Journal of Computer Applications},
	issue_date = {January 2018},
	volume = {179},
	number = {11},
	month = {Jan},
	year = {2018},
	issn = {0975-8887},
	pages = {15-19},
	numpages = {5},
	url = {},
	doi = {10.5120/ijca2018916083},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


In this paper work the association rules are optimized in order to find most suitable rules from the number of association rule generation algorithms. In this context most frequently used association rule mining algorithms are targeted for study namely Apriori and FP-tree. Basically the association rules are developed using transactional datasets. Additionally the number of generated rules in Apriori is large enough; on the other hand the FP-tree algorithm generates a main tree and additional trees. Such kind of tree generate confuse the experimenter. Therefore in this work a concept is proposed by which the optimal rules from both the set of rules are selected for applications.

In this context two different concepts of the rule selection techniques are used first technique usages the sampling technique and second directly usage the outcomes of the Apriori and FP-Tree algorithm and make search from one algorithm’s rule set to others. In order to perform the search genetic algorithm is used which is used for optimal solution selection. According to the results sampling based technique needs additional computational resources as compared to genetic algorithm based technique due to additional evaluation cycles. But both the algorithms are effectively capable to reduce the amount of rules generated by the selected algorithms.


  1. Dhanalakshmi. D and Dr. J. Komala Lakshmi, “A Survey on Data Mining Research Trends”, A Survey on Data Mining Research Trends, Volume 3, Issue 10 October, 2014 Page No. 8911-8919
  2. Berson, Alex, and Stephen J. Smith, Building data mining applications for CRM, McGraw-Hill, Inc., 2002.
  3. Agrawal, Rakesh, and Ramakrishnan Srikant, "Fast algorithms for mining association rules." Proc. 20th international conference very large data bases, VLDB, Volume 1215, 1994.
  4. “Association Rules Mining”, available online at:
  5. Rana Ishita and Rana Ishita, “Frequent Itemset Mining in Data Mining: A Survey”, International Journal of Computer Applications (IJCA), Volume 139 – No.9, April 2016
  6. Sanjaydeep Singh Lodhi and Premnarayan Arya, “Frequent Itemset Mining Technique in Data Mining”, International Journal of Advanced Research in Computer Engineering & Technology Volume 1, Issue 5, July 2012
  7. Borgelt, Christian. "Frequent item set mining", Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 2.6 (2012): pp. 437-456.


Data Mining, Association Rule, FP-Tee, Apriori, Frequent Pattern mining, Association Rule Mining, Genetic Algorithm