Research Article | OPEN ACCESS
Generating Frequent Patterns from Large Datasets using Improved Apriori and Support Chaining Method
1P. Alagesh Kannan and 2E. Ramaraj
1Department of Computer Science, Madurai Kamaraj University College, Madurai, Tamil Nadu, India
2Department of Computer Science and Engineering, Alagappa University, Karaikudi, Tamil Nadu, India
Research Journal of Applied Sciences, Engineering and Technology 2015 11:1281-1286
Received: December 4, 2014 | Accepted: April 1, 2015 | Published: August 15, 2015
Abstract
In this study, generating association rules with improved Apriori algorithm is proposed. Apriori is one of the most popular association rule mining algorithm that extracts frequent item sets from large databases. The traditional Apriori algorithm contains a major drawback. This algorithm wastes time in scanning the database to generate frequent item sets. The objective of any association rule mining algorithm is to generate association rules in a fast manner with great accuracy. In this study, a modification over the traditional Apriori algorithm is introduced. This improved Apriori algorithm searches frequent item sets from the large databases with less time. Experimental results shows that this improved Apriori algorithm reduces the scanning time as much as 67% and this algorithm is more efficient than the existing algorithm.
Keywords:
Apriori, ARM, , association rule mining, , ELCAT, , frequent pattern, , large datasets, , support chaining,
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PMCid:PMC2230637
Competing interests
The authors have no competing interests.
Open Access Policy
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Copyright
The authors have no competing interests.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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