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Mining Association Rules using R Environment

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Deepa U. Mishra, Nilam K. Kadale

Deepa U Mishra and Nilam K Kadale. Mining Association Rules using R Environment. International Journal of Computer Applications 157(4):45-50, January 2017. BibTeX

	author = {Deepa U. Mishra and Nilam K. Kadale},
	title = {Mining Association Rules using R Environment},
	journal = {International Journal of Computer Applications},
	issue_date = {January 2017},
	volume = {157},
	number = {4},
	month = {Jan},
	year = {2017},
	issn = {0975-8887},
	pages = {45-50},
	numpages = {6},
	url = {},
	doi = {10.5120/ijca2017912679},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


R is an unified collection of software attachments for performing various operations on data and graphical display. R has become a preferred platform for statistical analysis.

The R add-on package arules implements the basic infrastructure for creating and manipulating transaction databases and basic algorithms to efficiently find and analyze association rules. Compared to other tools, the arules framework is fully integrated, implements the latest approaches and has the vast functionality of R for further analysis of found patterns at its disposal. The “apriori” function, provided by the arules package, is used for mining association. Apriori Algorithm is the most popular algorithm for mining association rules. Association rule of data mining is employed in all tangible applications of business and industry. Objective of taking Apriori is to find frequent item sets and to disclose the unreleased information. This paper encompasses the use of association rule mining in extracting patterns that occur frequently within a dataset and showcases the importance of the Apriori algorithm in mining association rules from a dataset containing student information.


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Data Mining, The R Project, Association Rules, Apriori Algorithm.