Filtered-ARN: Asymmetric objective measures applied to filter Association Rules Networks

In this paper, the Filtered-Association Rules Network (Filtered-ARN) is presented to structure, prune, and analyze a set of association rules in order to construct candidate hypotheses. The Filtered-ARN algorithm selects association rules with the use of asymmetric objective measures, Added Value an...

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Bibliographic Details
Published inCLEI electronic journal Vol. 22; no. 3
Main Authors CALÇADA, DARIO BRITO, Rezende, Solange Oliveira
Format Journal Article
LanguageEnglish
Published Centro Latinoamericano de Estudios en Informática 01.12.2019
Subjects
Association rules
Association rules networks
Data mining
Graphs
Networks
Objective measures
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Summary:In this paper, the Filtered-Association Rules Network (Filtered-ARN) is presented to structure, prune, and analyze a set of association rules in order to construct candidate hypotheses. The Filtered-ARN algorithm selects association rules with the use of asymmetric objective measures, Added Value and Gain then builds a network allowing more exploration information. The Filtered-ARN was validated using three datasets: Lenses, Hayes-roth, and Soybean Large, available online. We carried out a concept proof experiment using a real dataset with data on organic fertilization (Green Manure) for text the proposed method. The results were validated by comparing the Filtered-ARN with the conventional ARN and also comparing the results with the decision tree. The approach presented promising results, showing its ability to explain a set of objective items and the aid to build more consolidated hypotheses by guaranteeing statistical dependence with the use of objective measures.
ISSN:0717-5000
0717-5000
DOI:10.19153/cleiej.22.3.2