Exploring the data using Extended Association Rule Network

dc.contributorUniversidade de São Paulo (USP)
dc.contributorItau Unibanco
dc.contributorUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2019-10-05T12:39:50Z
dc.date.accessioned2022-12-19T18:20:57Z
dc.date.available2019-10-05T12:39:50Z
dc.date.available2022-12-19T18:20:57Z
dc.date.created2019-10-05T12:39:50Z
dc.date.issued2018-01-01
dc.identifier2018 7th Brazilian Conference On Intelligent Systems (bracis). New York: Ieee, p. 330-335, 2018.
dc.identifierhttp://hdl.handle.net/11449/186619
dc.identifier10.1109/BRACIS.2018.00064
dc.identifierWOS:000457627300056
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/5367657
dc.description.abstractIn 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.
dc.languageeng
dc.publisherIeee
dc.relation2018 7th Brazilian Conference On Intelligent Systems (bracis)
dc.rightsAcesso aberto
dc.sourceWeb of Science
dc.subjectAssociation Rules
dc.subjectAssociation Rules Network
dc.subjectHypothesis building
dc.subjectData Analysis and Market Basket Analysis
dc.titleExploring the data using Extended Association Rule Network
dc.typeActas de congresos


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