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Analysis of MFGA to Extract Interesting Rules

by Mrinalini Rana, P S Mann
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 84 - Number 3
Year of Publication: 2013
Authors: Mrinalini Rana, P S Mann
10.5120/14555-2653

Mrinalini Rana, P S Mann . Analysis of MFGA to Extract Interesting Rules. International Journal of Computer Applications. 84, 3 ( December 2013), 15-21. DOI=10.5120/14555-2653

@article{ 10.5120/14555-2653,
author = { Mrinalini Rana, P S Mann },
title = { Analysis of MFGA to Extract Interesting Rules },
journal = { International Journal of Computer Applications },
issue_date = { December 2013 },
volume = { 84 },
number = { 3 },
month = { December },
year = { 2013 },
issn = { 0975-8887 },
pages = { 15-21 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume84/number3/14555-2653/ },
doi = { 10.5120/14555-2653 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:59:57.997924+05:30
%A Mrinalini Rana
%A P S Mann
%T Analysis of MFGA to Extract Interesting Rules
%J International Journal of Computer Applications
%@ 0975-8887
%V 84
%N 3
%P 15-21
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper presents a Genetic algorithm based association rule mining in which multi fitness functions are used. Genetic algorithm is used for performing global search. This proposed algorithm generates intersecting association rules from dataset. A fitness function with parameter support is defined for generating frequent itemsets and then other parameters like confidence, lift, leverage etc are used for defining second fitness function for generating association rules. The proposed algorithm is compared with classical Apriori algorithm and also with existing Genetic algorithm for association rule mining on the basis of metrics Support Count, Confidence count, and rule accuracy. Comparisons are also made on different generations.

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Index Terms

Computer Science
Information Sciences

Keywords

Multi-Fitness Function Genetic algorithm (MFGA) Apriori algorithm Genetic Algorithm Crossover Probability Fitness function Support Confidence Lift Leverage Coverage.

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