Apriori-Roaring-Parallel: Frequent pattern mining based on compressed bitmaps with OpenMP

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorFaculty of Business-
Autor(es): dc.creatorColombo, Alexandre-
Autor(es): dc.creatorSpolon, Roberta-
Autor(es): dc.creatorLobato, Renata Spolon-
Autor(es): dc.creatorManacero, Aleardo-
Autor(es): dc.creatorCavenaghi, Marcos Antonio-
Data de aceite: dc.date.accessioned2025-08-21T22:32:33Z-
Data de disponibilização: dc.date.available2025-08-21T22:32:33Z-
Data de envio: dc.date.issued2022-04-29-
Data de envio: dc.date.issued2022-04-29-
Data de envio: dc.date.issued2020-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1109/ISCC53001.2021.9631495-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/230253-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/230253-
Descrição: dc.descriptionMining association rules is a process which consists in extracting knowledge from datasets. This is a widely used technique to analyze customer purchasing patterns, and its process is segmented in two main phases: mining frequent sets and formulating association rules. Several approaches were developed for the first phase of the mining process whose main objective was to reduce execution time. However, as all available datasets are very large (Big Data), there is a limitation regarding its application in these new sets due to excessive memory usage. We propose the Apriori-Roaring-Parallel which explores parallelism in shared memory and demands less memory usage during the mining process. In order to achieve this memory usage reduction, the Apriori-Roaring-Parallel method employs compressed bitmap structures to represent the datasets. The results obtained show that the Apriori-Roaring-Parallel method uses memory efficiently when compared to other methods.-
Descrição: dc.descriptionSão Paulo State University UNESP Computing Department-
Descrição: dc.descriptionSão Paulo State University UNESP Department of Computer Science And Statistics, São José do Rio Preto-
Descrição: dc.descriptionHumber Institute of Technology And Advanced Learning Faculty of Business-
Descrição: dc.descriptionSão Paulo State University UNESP Computing Department-
Descrição: dc.descriptionSão Paulo State University UNESP Department of Computer Science And Statistics, São José do Rio Preto-
Idioma: dc.languageen-
Relação: dc.relationProceedings - IEEE Symposium on Computers and Communications-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectAssociation Rules-
Palavras-chave: dc.subjectBitmap Compression-
Palavras-chave: dc.subjectData Mining-
Palavras-chave: dc.subjectIdentification of Frequent Sets-
Título: dc.titleApriori-Roaring-Parallel: Frequent pattern mining based on compressed bitmaps with OpenMP-
Tipo de arquivo: dc.typeaula digital-
Aparece nas coleções:Repositório Institucional - Unesp

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