Apriori-roaring: frequent pattern mining method based on compressed bitmaps
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1504/IJBIDM.2022.123805 http://hdl.handle.net/11449/241304 |
Resumo: | Association 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. |
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Repositório Institucional da UNESP |
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2946 |
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Apriori-roaring: frequent pattern mining method based on compressed bitmapsassociation rulesbitmap compressiondata miningfrequent pattern miningknowledge discoveryAssociation 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.Computing Department São Paulo State University, Bauru, SPDepartment of Computer Science and Statistics São Paulo State University, São José do Rio Preto, SPFaculty of Business Humber Institute of Technology and Advanced LearningComputing Department São Paulo State University, Bauru, SPDepartment of Computer Science and Statistics São Paulo State University, São José do Rio Preto, SPUniversidade Estadual Paulista (UNESP)Humber Institute of Technology and Advanced LearningColombo, Alexandre [UNESP]Spolon, Roberta [UNESP]Junior, Aleardo Manacero [UNESP]Lobato, Renata Spolon [UNESP]Cavenaghi, Marcos Antônio2023-03-01T20:56:06Z2023-03-01T20:56:06Z2022-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article48-65http://dx.doi.org/10.1504/IJBIDM.2022.123805International Journal of Business Intelligence and Data Mining, v. 21, n. 1, p. 48-65, 2022.1743-81951743-8187http://hdl.handle.net/11449/24130410.1504/IJBIDM.2022.1238052-s2.0-85133778868Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengInternational Journal of Business Intelligence and Data Mininginfo:eu-repo/semantics/openAccess2024-04-23T16:11:00Zoai:repositorio.unesp.br:11449/241304Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T20:32:44.590317Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Apriori-roaring: frequent pattern mining method based on compressed bitmaps |
title |
Apriori-roaring: frequent pattern mining method based on compressed bitmaps |
spellingShingle |
Apriori-roaring: frequent pattern mining method based on compressed bitmaps Colombo, Alexandre [UNESP] association rules bitmap compression data mining frequent pattern mining knowledge discovery |
title_short |
Apriori-roaring: frequent pattern mining method based on compressed bitmaps |
title_full |
Apriori-roaring: frequent pattern mining method based on compressed bitmaps |
title_fullStr |
Apriori-roaring: frequent pattern mining method based on compressed bitmaps |
title_full_unstemmed |
Apriori-roaring: frequent pattern mining method based on compressed bitmaps |
title_sort |
Apriori-roaring: frequent pattern mining method based on compressed bitmaps |
author |
Colombo, Alexandre [UNESP] |
author_facet |
Colombo, Alexandre [UNESP] Spolon, Roberta [UNESP] Junior, Aleardo Manacero [UNESP] Lobato, Renata Spolon [UNESP] Cavenaghi, Marcos Antônio |
author_role |
author |
author2 |
Spolon, Roberta [UNESP] Junior, Aleardo Manacero [UNESP] Lobato, Renata Spolon [UNESP] Cavenaghi, Marcos Antônio |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (UNESP) Humber Institute of Technology and Advanced Learning |
dc.contributor.author.fl_str_mv |
Colombo, Alexandre [UNESP] Spolon, Roberta [UNESP] Junior, Aleardo Manacero [UNESP] Lobato, Renata Spolon [UNESP] Cavenaghi, Marcos Antônio |
dc.subject.por.fl_str_mv |
association rules bitmap compression data mining frequent pattern mining knowledge discovery |
topic |
association rules bitmap compression data mining frequent pattern mining knowledge discovery |
description |
Association 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. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-01-01 2023-03-01T20:56:06Z 2023-03-01T20:56:06Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1504/IJBIDM.2022.123805 International Journal of Business Intelligence and Data Mining, v. 21, n. 1, p. 48-65, 2022. 1743-8195 1743-8187 http://hdl.handle.net/11449/241304 10.1504/IJBIDM.2022.123805 2-s2.0-85133778868 |
url |
http://dx.doi.org/10.1504/IJBIDM.2022.123805 http://hdl.handle.net/11449/241304 |
identifier_str_mv |
International Journal of Business Intelligence and Data Mining, v. 21, n. 1, p. 48-65, 2022. 1743-8195 1743-8187 10.1504/IJBIDM.2022.123805 2-s2.0-85133778868 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
International Journal of Business Intelligence and Data Mining |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
48-65 |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808129217567653888 |