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Reseach Article

Classifying Association Rules with Minimized Sets using Fuzzy-Aprioi Algorithm

by Sonam Jain, Anand Rajawat
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 128 - Number 5
Year of Publication: 2015
Authors: Sonam Jain, Anand Rajawat
10.5120/ijca2015906560

Sonam Jain, Anand Rajawat . Classifying Association Rules with Minimized Sets using Fuzzy-Aprioi Algorithm. International Journal of Computer Applications. 128, 5 ( October 2015), 41-47. DOI=10.5120/ijca2015906560

@article{ 10.5120/ijca2015906560,
author = { Sonam Jain, Anand Rajawat },
title = { Classifying Association Rules with Minimized Sets using Fuzzy-Aprioi Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { October 2015 },
volume = { 128 },
number = { 5 },
month = { October },
year = { 2015 },
issn = { 0975-8887 },
pages = { 41-47 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume128/number5/22873-2015906560/ },
doi = { 10.5120/ijca2015906560 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:22:07.912602+05:30
%A Sonam Jain
%A Anand Rajawat
%T Classifying Association Rules with Minimized Sets using Fuzzy-Aprioi Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 128
%N 5
%P 41-47
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Using Association Rule mining is extremely well-organized method for getting strong relation between correlated data or information. The correlation of data provides significance complete taking out progression. For the mining of positive and negative rules, a range of algorithms are utilized for example Apriori algorithm and tree based algorithm. A numeral of algorithms is be unsure presentation but manufactures huge number of negative association rule and also goes through from multi-scan difficulty. The proposal of is to get rid of these difficulties and decrease huge amount of negative rules. Here an efficient technique is implemented for the classification of association rules generated using Fuzzy-Apriori algorithm and classification of these rules can be done supervised learning such as Naïve Bayes Algorithm. The proposed methodology implemented here provides efficient results as compared to the existing technique implemented for the generation of association rules.

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Index Terms

Computer Science
Information Sciences

Keywords

Association Rules CART Naïve Bayes Decision Tree Frequent Item sets In-frequent Item Sets Positive Rules Negative Rules.