Optimized Association Rule Mining with Maximum Constraints using Genetic Algorithm
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2015, Vol 3, Issue 4
Abstract
Association rule mining is to find out association rules that satisfy the predefined minimum support and confidence from a given database.. In this paper initially we applied Apriori algorithm in order to generate frequent item-sets and then frequent item-sets are used to generate association rules. After getting association rules from Apriori algorithm we applied Genetic Algorithm (GA) to obtain reduced number of association rules. For this we used Selection, Genetic Operators. We designed a new fitness function for the proposed algorithm. It is observed that this algorithm greatly reduces the problem of generation of huge association rules using Apriori algorithm. The implementation of the proposed algorithm is easier than other popular algorithm for association rule mining. The proposed algorithm performs much better when compared to Apriori algorithm and other previous technique used to optimize association rule mining.
Authors and Affiliations
Rajdeep Kaur Aulakh
Keywords
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