Ranking association rules by clustering through interestingness

Registro completo de metadados
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorde Carvalho, Veronica Oliveira-
Autor(es): dc.creatorde Paula, Davi Duarte-
Autor(es): dc.creatorPacheco, Mateus Violante-
Autor(es): dc.creatordos Santos, Waldeilson Eder-
Autor(es): dc.creatorde Padua, Renan-
Autor(es): dc.creatorRezende, Solange Oliveira-
Data de aceite: dc.date.accessioned2021-03-11T01:32:03Z-
Data de disponibilização: dc.date.available2021-03-11T01:32:03Z-
Data de envio: dc.date.issued2019-10-06-
Data de envio: dc.date.issued2019-10-06-
Data de envio: dc.date.issued2018-01-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1007/978-3-030-02837-4_28-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/187266-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/187266-
Descrição: dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
Descrição: dc.descriptionProcesso FAPESP: 2015/08059-0-
Descrição: dc.descriptionThe association rules (ARs) post-processing step is challenging, since many patterns are extracted and only a few of them are useful to the user. One of the most traditional approaches to find rules that are of interestingness is the use of objective measures (OMs). Due to their frequent use, many of them exist (over 50). Therefore, when a user decides to apply such strategy he has to decide which one to use. To solve this problem this work proposes a process to cluster ARs based on their interestingness, according to a set of OMs, to obtain an ordered list containing the most relevant patterns. That way, the user does not need to know which OM to use/select nor to handle the output of different OMs lists. Experiments show that the proposed process behaves equal or better than as if the best OM had been used.-
Formato: dc.format336-351-
Idioma: dc.languageen-
Relação: dc.relationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)-
Direitos: dc.rightsopenAccess-
Palavras-chave: dc.subjectAssociation rules-
Palavras-chave: dc.subjectClustering-
Palavras-chave: dc.subjectObjective measures-
Palavras-chave: dc.subjectPost-processing-
Título: dc.titleRanking association rules by clustering through interestingness-
Aparece nas coleções:Repositório Institucional - Unesp

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