Apriori-roaring: frequent pattern mining method based on compressed bitmaps

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorHumber Institute of Technology and Advanced Learning-
Autor(es): dc.creatorColombo, Alexandre-
Autor(es): dc.creatorSpolon, Roberta-
Autor(es): dc.creatorJunior, Aleardo Manacero-
Autor(es): dc.creatorLobato, Renata Spolon-
Autor(es): dc.creatorCavenaghi, Marcos Antônio-
Data de aceite: dc.date.accessioned2025-08-21T20:39:29Z-
Data de disponibilização: dc.date.available2025-08-21T20:39:29Z-
Data de envio: dc.date.issued2023-03-01-
Data de envio: dc.date.issued2023-03-01-
Data de envio: dc.date.issued2021-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1504/IJBIDM.2022.123805-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/241304-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/241304-
Descrição: dc.descriptionAssociation rule mining is one of the most common tasks in data analysis. It has a descriptive feature used to discover patterns in sets of data. Most existing approaches to data analysis have a constraint related to execution time. However, as the size of datasets used in the analysis grows, memory usage tends to be the constraint instead, and this prevents these approaches from being used. This article presents a new method for data analysis called apriori-roaring. The apriori-roaring method is designed to identify frequent items with a focus on scalability. The implementation of this method employs compressed bitmap structures, which use less memory to store the original dataset and to calculate the support metric. The results show that apriori-roaring allows the identification of frequent elements with much lower memory usage and shorter execution time. In terms of scalability, our proposed approach outperforms the various traditional approaches available.-
Descrição: dc.descriptionComputing Department São Paulo State University, Bauru, SP-
Descrição: dc.descriptionDepartment of Computer Science and Statistics São Paulo State University, São José do Rio Preto, SP-
Descrição: dc.descriptionFaculty of Business Humber Institute of Technology and Advanced Learning-
Descrição: dc.descriptionComputing Department São Paulo State University, Bauru, SP-
Descrição: dc.descriptionDepartment of Computer Science and Statistics São Paulo State University, São José do Rio Preto, SP-
Formato: dc.format48-65-
Idioma: dc.languageen-
Relação: dc.relationInternational Journal of Business Intelligence and Data Mining-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectassociation rules-
Palavras-chave: dc.subjectbitmap compression-
Palavras-chave: dc.subjectdata mining-
Palavras-chave: dc.subjectfrequent pattern mining-
Palavras-chave: dc.subjectknowledge discovery-
Título: dc.titleApriori-roaring: frequent pattern mining method based on compressed bitmaps-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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