Exploring the Data Using Extended Association Rule Network

Renan de Padua, Dario Brito Calçada, Verônica Oliveira de Carvalho, Solange Oliveira Rezende
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引用次数: 3

Abstract

In this paper, we presented the Extended Association Rule Network (ExARN) to structure, prune and analyze a set of association rules, aiming to build hypothesis candidates. The ExARN extends the ARN, proposed by [2], allowing a more complete exploration. We validate the ExARN using two databases: contact lenses and hayes-roth, both available online for download. The results were validated by comparing the ExARN to the conventional ARN and also by comparing the results with a decision tree algorithms. The approach presented promising results, showing its capability to explain a set of objective items, aiding the user on the hypothesis building.
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利用扩展关联规则网络探索数据
在本文中,我们提出了扩展关联规则网络(ExARN)来构建、修剪和分析一组关联规则,旨在建立假设候选。ExARN扩展了[2]提出的ARN,允许更完整的探索。我们使用两个数据库来验证ExARN:隐形眼镜数据库和hayes-roth数据库,这两个数据库都可以在线下载。通过将ExARN与传统的ARN进行比较,并将结果与决策树算法进行比较,验证了结果。该方法呈现出令人鼓舞的结果,显示出其解释一组客观项目的能力,帮助用户建立假设。
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