文章基本信息

标题：Filtered-ARN: Asymmetric objective measures applied to filter Association Rules Networks
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作者：DARIO BRITO CALÇADA ; Solange Oliveira Rezende
期刊名称：CLEI Electronic Journal
印刷版ISSN：0717-5000
出版年度：2019
卷号：22
期号：3
DOI：10.19153/cleiej.22.3.2
语种：English
出版社：Centro Latinoamericano de Estudios en Informática
摘要：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.