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An Efficient Method for Generating Local Association Rules

F. A. Mazarbhuiya, Yusuf Perwej Published in Artificial Intelligence

International Journal of Applied Information Systems
Year of Publication: 2015
© 2015 by IJAIS Journal
10.5120/ijais15-451368
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  1. F A Mazarbhuiya and Yusuf Perwej. Article: An Efficient Method for Generating Local Association Rules. International Journal of Applied Information Systems 9(2):1-5, June 2015. BibTeX

    @article{key:article,
    	author = "F. A. Mazarbhuiya and Yusuf Perwej",
    	title = "Article: An Efficient Method for Generating Local Association Rules",
    	journal = "International Journal of Applied Information Systems",
    	year = 2015,
    	volume = 9,
    	number = 2,
    	pages = "1-5",
    	month = "June",
    	note = "Published by Foundation of Computer Science, New York, USA"
    }
    

Abstract

Carving association rules from any available set is a pre-defined problem and there are a variety of methods available for the extraction of association rules. Almost in all the cases the major emphasis is given to the generating most occurring itemsets rather than the extraction of association rules. Only a few numbers of researchers have through some light on this problem. Extracting generally most occurring itemsets for sometimes does not guarantee that particular dataset will occur in its lifetime. . For finding local association rules of the form A Þ X - A, where X and A are itemsets that hold in the interval [t, t¢] and . In order to calculate the confidence of the rule A Þ X – A in the interval [t, t¢], it is required to know the supports of both X and A in the same interval [t, t¢]. The supports of X and any of its subset A may not be available for the same time interval - A may be frequent in an interval greater than [t, t¢]. So, they have loosely defined local association rule as confidence in the rule A Þ X – A can't be calculated in interval [t, t¢]. In this paper, we present a latest approach for finding association rules from generally most occurring item sets using Rough Set and Boolean reasoning. The rules carved are called local association rules. The efficacy of the proposed approach is established through experiment over retail dataset that contains retail market basket data from an anonymous Belgian retail store.

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Keywords

Data Mining, Temporal Data Mining, Local Association Rule Mining, Rough Set, Boolean Reasoning