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Metadados | Descrição | Idioma |
---|---|---|
Autor(es): dc.contributor | Universidade de São Paulo (USP) | - |
Autor(es): dc.contributor | Universidade Estadual Paulista (Unesp) | - |
Autor(es): dc.creator | Padua, Renan de | - |
Autor(es): dc.creator | Carmo, Lais Pessine do | - |
Autor(es): dc.creator | Rezende, Solange Oliveira | - |
Autor(es): dc.creator | Carvalho, Veronica Oliveira de [UNESP] | - |
Autor(es): dc.creator | IEEE | - |
Data de aceite: dc.date.accessioned | 2022-02-22T01:01:55Z | - |
Data de disponibilização: dc.date.available | 2022-02-22T01:01:55Z | - |
Data de envio: dc.date.issued | 2021-06-25 | - |
Data de envio: dc.date.issued | 2021-06-25 | - |
Data de envio: dc.date.issued | 2018-01-01 | - |
Fonte completa do material: dc.identifier | http://hdl.handle.net/11449/210529 | - |
Fonte: dc.identifier.uri | http://educapes.capes.gov.br/handle/11449/210529 | - |
Descrição: dc.description | Association rules are widely used to extract patterns from a given database. The association rules are capable of finding correlations among items, making it possible for the user to learn which items are present in the transactions and which of them have a significant correlation. One of the major problems with association rules is that the number of extracted rules usually exceeds the number of transactions present in the database, also surpassing the user's capability to explore the obtained knowledge. To overcome this problem, the post-processing phase was proposed with the objective of directing the user to the rules that potentially have the most interesting knowledge. One of the used approaches is to divide the association rules into groups (or clusters), so that rules behave similarly are on the same group, facilitating the rule set understanding. In the literature, there are some works that uses clustering algorithms to split the rules while some other works use community detection algorithms. As both approaches obtain groups of association rules, but using different premises, different results can be obtained. No study has been done on the differences among clustering and community detection algorithms, which makes the selection of the algorithm hard, once their behavior is not well known in the association rule post-processing phase. This paper presents an analysis on both approaches, aiming to find the differences and the similarities among them, making it easier to select an approach by knowing its behavior. | - |
Descrição: dc.description | Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) | - |
Descrição: dc.description | Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) | - |
Descrição: dc.description | Univ Sao Paulo, Sao Carlos, Brazil | - |
Descrição: dc.description | UNESP Sao Paulo State Univ, Rio Claro, Brazil | - |
Descrição: dc.description | UNESP Sao Paulo State Univ, Rio Claro, Brazil | - |
Descrição: dc.description | FAPESP: 2016/17078-0 | - |
Formato: dc.format | 7 | - |
Idioma: dc.language | en | - |
Publicador: dc.publisher | Ieee | - |
Relação: dc.relation | 2018 International Joint Conference On Neural Networks (ijcnn) | - |
???dc.source???: dc.source | Web of Science | - |
Título: dc.title | An Analysis on Community Detection and Clustering Algorithms on the Post-Processing of Association Rules | - |
Aparece nas coleções: | Repositório Institucional - Unesp |
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