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Ordered Frequent Itemsets Matrix Based On Fp-Tree Structure And Apriori Algorithm

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Discovering Interesting Association Rules: A Multi-objective Genetic Algorithm Approach

by Basheer Mohamad Al-Maqaleh
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International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 5 - Number 3
Year of Publication: 2013
Authors: Basheer Mohamad Al-Maqaleh
10.5120/ijais12-450873

Basheer Mohamad Al-Maqaleh . Discovering Interesting Association Rules: A Multi-objective Genetic Algorithm Approach. International Journal of Applied Information Systems. 5, 3 ( February 2013), 47-52. DOI=10.5120/ijais12-450873

@article{ 10.5120/ijais12-450873,
author = { Basheer Mohamad Al-Maqaleh },
title = { Discovering Interesting Association Rules: A Multi-objective Genetic Algorithm Approach },
journal = { International Journal of Applied Information Systems },
issue_date = { February 2013 },
volume = { 5 },
number = { 3 },
month = { February },
year = { 2013 },
issn = { 2249-0868 },
pages = { 47-52 },
numpages = {9},
url = { https://www.ijais.org/archives/volume5/number3/430-0873/ },
doi = { 10.5120/ijais12-450873 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-07-05T17:58:18.171300+05:30
%A Basheer Mohamad Al-Maqaleh
%T Discovering Interesting Association Rules: A Multi-objective Genetic Algorithm Approach
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 5
%N 3
%P 47-52
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Association rule mining is considered as one of the important tasks of data mining intended towards decision making process. It has been mainly developed to identify interesting associations and/or correlation relationships between frequent itemsets in datasets. A multi-objective genetic algorithm approach is proposed in this paper for the discovery of interesting association rules with multiple criteria i. e. support, confidence and simplicity (comprehensibility). With Genetic Algorithm (GA), a global search can be achieved and system automation is developed, because the proposed algorithm could identify interesting association rules from a dataset without having the user-specified thresholds of minimum support and minimum confidence. The experimental results on various types of datasets show the usefulness and effectiveness of the proposed algorithm.

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Index Terms

Computer Science
Information Sciences

Keywords

Association Rule Interestingness Measure Genetic Operators Frequent Itemsets

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