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Reseach Article

An Optimized Association Rule mining using Genetic Algorithm

by Dimple S. Kanani, Shailendra K. Mishra
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 119 - Number 14
Year of Publication: 2015
Authors: Dimple S. Kanani, Shailendra K. Mishra
10.5120/21134-4063

Dimple S. Kanani, Shailendra K. Mishra . An Optimized Association Rule mining using Genetic Algorithm. International Journal of Computer Applications. 119, 14 ( June 2015), 11-15. DOI=10.5120/21134-4063

@article{ 10.5120/21134-4063,
author = { Dimple S. Kanani, Shailendra K. Mishra },
title = { An Optimized Association Rule mining using Genetic Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { June 2015 },
volume = { 119 },
number = { 14 },
month = { June },
year = { 2015 },
issn = { 0975-8887 },
pages = { 11-15 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume119/number14/21134-4063/ },
doi = { 10.5120/21134-4063 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:04:00.638859+05:30
%A Dimple S. Kanani
%A Shailendra K. Mishra
%T An Optimized Association Rule mining using Genetic Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 119
%N 14
%P 11-15
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Association Rule Mining technique that attempt to unearthing interesting pattern or relationship between data in large Database. Genetic Algorithm is a search heuristic which is used to generate useful solution for optimization and search problems. Genetic Algorithm based evaluation in Mining Technique is backbone for mining interesting Rule based on GA parameters like fitness function, Crossover Rate, Mutation Rate. The key focus of this synthesize approach is to optimize the rule that generated by mining methodology and to provide more accurate results. The Proposed Approach is to generate rules based on Quantitative dataset, using the concept of threshold - frequent item sets are define as initial population which the first step of Genetic algorithm. Crossover & mutation is applied to generate more combination of rule & can identify Co-occurrence of item sets.

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Index Terms

Computer Science
Information Sciences

Keywords

Association rule Mining Genetic Algorithm Data mining and Optimization.