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Reseach Article

Optimization of Fragment based Mining through Genetic Algorithm

by Rajesh V. Argiddi, S. S. Apte
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 100 - Number 13
Year of Publication: 2014
Authors: Rajesh V. Argiddi, S. S. Apte
10.5120/17589-8299

Rajesh V. Argiddi, S. S. Apte . Optimization of Fragment based Mining through Genetic Algorithm. International Journal of Computer Applications. 100, 13 ( August 2014), 37-42. DOI=10.5120/17589-8299

@article{ 10.5120/17589-8299,
author = { Rajesh V. Argiddi, S. S. Apte },
title = { Optimization of Fragment based Mining through Genetic Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { August 2014 },
volume = { 100 },
number = { 13 },
month = { August },
year = { 2014 },
issn = { 0975-8887 },
pages = { 37-42 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume100/number13/17589-8299/ },
doi = { 10.5120/17589-8299 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:29:54.873350+05:30
%A Rajesh V. Argiddi
%A S. S. Apte
%T Optimization of Fragment based Mining through Genetic Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 100
%N 13
%P 37-42
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The approach stated in this paper mainly focuses on generating optimized rules in fragment based association mining using genetic algorithm. we call this approach as Genetic based Fragment Rule Mining. we designed a novel method for generation of optimized rule. In which a Fragment mining is used to generate the rules on which we use the optimization mechanism. This deals mainly with reducing the time and space complexity required in processing the data using fragment mining & generate strong rules using genetic algorithm. The results reported in this paper are very promising since the discovered rules are of optimized rules.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Association Rule Fragment Mining Stock Data Genetic Algorithm.