An Analytical Study of Genetic Algorithm for Generating Frequent Itemset and Framing Association Rules At Various Support Levels
Journal Title: IOSR Journals (IOSR Journal of Computer Engineering) - Year 2016, Vol 18, Issue 4
Abstract
Abstract: In customary, frequent itemsets are propogated from large data sets by employing association rule mining algorithms like Apriori, Partition, Pincer-Search, Incremental and Border algorithm etc., which gains inordinately longer computer time to cast up all the frequent itemsets. On utilizing Genetic Algorithm (GA) the scheme is reformed.. The outstanding benefit of utilizing GA in determining the frequent itemsets is to discharge exhaustive survey and its time convolution subsides in collation with other algorithms, since GA is built on the greedy mode. The effective plan of this report is to detect all the frequent itemsets and to generate the association rules at various levels of minimum support and confidence defined by the user, with very less time and less memory from the furnished data sets using genetic algorithm.
Authors and Affiliations
D. Ashok Kumar , T. A. usha
Keywords
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- EP ID EP96297
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