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An Optimization of Association Rule Mining using K-Map and Genetic Algorithm for Large Database

by Ghanshyam Dhanore, Setu Kumar Chaturvedi
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 84 - Number 17
Year of Publication: 2013
Authors: Ghanshyam Dhanore, Setu Kumar Chaturvedi
10.5120/14680-2143

Ghanshyam Dhanore, Setu Kumar Chaturvedi . An Optimization of Association Rule Mining using K-Map and Genetic Algorithm for Large Database. International Journal of Computer Applications. 84, 17 ( December 2013), 26-31. DOI=10.5120/14680-2143

@article{ 10.5120/14680-2143,
author = { Ghanshyam Dhanore, Setu Kumar Chaturvedi },
title = { An Optimization of Association Rule Mining using K-Map and Genetic Algorithm for Large Database },
journal = { International Journal of Computer Applications },
issue_date = { December 2013 },
volume = { 84 },
number = { 17 },
month = { December },
year = { 2013 },
issn = { 0975-8887 },
pages = { 26-31 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume84/number17/14680-2143/ },
doi = { 10.5120/14680-2143 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:01:12.272832+05:30
%A Ghanshyam Dhanore
%A Setu Kumar Chaturvedi
%T An Optimization of Association Rule Mining using K-Map and Genetic Algorithm for Large Database
%J International Journal of Computer Applications
%@ 0975-8887
%V 84
%N 17
%P 26-31
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Rule mining is very efficient technique for find relation of correlated data. The correlation of data gives meaning full extraction process. For the mining of rule mining a variety of algorithm are used such as Apriori algorithm and tree based algorithm. Some algorithm is wonder performance but generate negative association rule and also suffered from multi-scan problem. In this paper we proposed a k-apriori-GA association rule mining based on genetic algorithm and K-map formula. In this method we used a k-map binary table for partition of data table as 0 and 1. The divided process reduces the scanning time of database. The proposed algorithm is a combination of k-partition and near distance of k-map candidate key. Support weight key is a vector value given by the transaction data set. The process of rule optimization we used genetic algorithm and for evaluate algorithm conducted the real world dataset The National Rural Employment Guarantee Act (NREGA) Department of Rural Development Government of India.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Association rule mining negative and positive rules multi-pass k-map Genetic algorithm.

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