Multi-relational algorithm for mining association rules in large databases

Detalhes bibliográficos
Autor(a) principal: Valêncio, Carlos Roberto [UNESP]
Data de Publicação: 2011
Outros Autores: Oyama, Fernando Takeshi [UNESP], Ichiba, Fernando Tochio [UNESP], De Souza, Rogéria Cristiane Gratão [UNESP]
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1109/PDCAT.2011.56
http://hdl.handle.net/11449/72859
Resumo: Multi-relational data mining enables pattern mining from multiple tables. The existing multi-relational mining association rules algorithms are not able to process large volumes of data, because the amount of memory required exceeds the amount available. The proposed algorithm MRRadix presents a framework that promotes the optimization of memory usage. It also uses the concept of partitioning to handle large volumes of data. The original contribution of this proposal is enable a superior performance when compared to other related algorithms and moreover successfully concludes the task of mining association rules in large databases, bypass the problem of available memory. One of the tests showed that the MR-Radix presents fourteen times less memory usage than the GFP-growth. © 2011 IEEE.
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spelling Multi-relational algorithm for mining association rules in large databasesAssociation rulesFrequent itemsets miningMulti-relational data miningRelational databaseItem setsLarge databaseMemory usageMining associationsMultirelational data miningPattern miningRelational DatabaseAlgorithmsData miningDatabase systemsMulti-relational data mining enables pattern mining from multiple tables. The existing multi-relational mining association rules algorithms are not able to process large volumes of data, because the amount of memory required exceeds the amount available. The proposed algorithm MRRadix presents a framework that promotes the optimization of memory usage. It also uses the concept of partitioning to handle large volumes of data. The original contribution of this proposal is enable a superior performance when compared to other related algorithms and moreover successfully concludes the task of mining association rules in large databases, bypass the problem of available memory. One of the tests showed that the MR-Radix presents fourteen times less memory usage than the GFP-growth. © 2011 IEEE.Depto. de Ciências de Computação e Estatística Universidade Estadual Paulista - Unesp, São José do Rio PretoDepto. de Ciências de Computação e Estatística Universidade Estadual Paulista - Unesp, São José do Rio PretoUniversidade Estadual Paulista (Unesp)Valêncio, Carlos Roberto [UNESP]Oyama, Fernando Takeshi [UNESP]Ichiba, Fernando Tochio [UNESP]De Souza, Rogéria Cristiane Gratão [UNESP]2014-05-27T11:26:14Z2014-05-27T11:26:14Z2011-12-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject269-274http://dx.doi.org/10.1109/PDCAT.2011.56Parallel and Distributed Computing, Applications and Technologies, PDCAT Proceedings, p. 269-274.http://hdl.handle.net/11449/7285910.1109/PDCAT.2011.562-s2.0-84856658056464481225387583259146517545178640000-0002-9325-31590000-0002-7449-9022Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengParallel and Distributed Computing, Applications and Technologies, PDCAT Proceedingsinfo:eu-repo/semantics/openAccess2021-10-23T10:11:03Zoai:repositorio.unesp.br:11449/72859Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T18:27:52.249263Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Multi-relational algorithm for mining association rules in large databases
title Multi-relational algorithm for mining association rules in large databases
spellingShingle Multi-relational algorithm for mining association rules in large databases
Valêncio, Carlos Roberto [UNESP]
Association rules
Frequent itemsets mining
Multi-relational data mining
Relational database
Item sets
Large database
Memory usage
Mining associations
Multirelational data mining
Pattern mining
Relational Database
Algorithms
Data mining
Database systems
title_short Multi-relational algorithm for mining association rules in large databases
title_full Multi-relational algorithm for mining association rules in large databases
title_fullStr Multi-relational algorithm for mining association rules in large databases
title_full_unstemmed Multi-relational algorithm for mining association rules in large databases
title_sort Multi-relational algorithm for mining association rules in large databases
author Valêncio, Carlos Roberto [UNESP]
author_facet Valêncio, Carlos Roberto [UNESP]
Oyama, Fernando Takeshi [UNESP]
Ichiba, Fernando Tochio [UNESP]
De Souza, Rogéria Cristiane Gratão [UNESP]
author_role author
author2 Oyama, Fernando Takeshi [UNESP]
Ichiba, Fernando Tochio [UNESP]
De Souza, Rogéria Cristiane Gratão [UNESP]
author2_role author
author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (Unesp)
dc.contributor.author.fl_str_mv Valêncio, Carlos Roberto [UNESP]
Oyama, Fernando Takeshi [UNESP]
Ichiba, Fernando Tochio [UNESP]
De Souza, Rogéria Cristiane Gratão [UNESP]
dc.subject.por.fl_str_mv Association rules
Frequent itemsets mining
Multi-relational data mining
Relational database
Item sets
Large database
Memory usage
Mining associations
Multirelational data mining
Pattern mining
Relational Database
Algorithms
Data mining
Database systems
topic Association rules
Frequent itemsets mining
Multi-relational data mining
Relational database
Item sets
Large database
Memory usage
Mining associations
Multirelational data mining
Pattern mining
Relational Database
Algorithms
Data mining
Database systems
description Multi-relational data mining enables pattern mining from multiple tables. The existing multi-relational mining association rules algorithms are not able to process large volumes of data, because the amount of memory required exceeds the amount available. The proposed algorithm MRRadix presents a framework that promotes the optimization of memory usage. It also uses the concept of partitioning to handle large volumes of data. The original contribution of this proposal is enable a superior performance when compared to other related algorithms and moreover successfully concludes the task of mining association rules in large databases, bypass the problem of available memory. One of the tests showed that the MR-Radix presents fourteen times less memory usage than the GFP-growth. © 2011 IEEE.
publishDate 2011
dc.date.none.fl_str_mv 2011-12-01
2014-05-27T11:26:14Z
2014-05-27T11:26:14Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/conferenceObject
format conferenceObject
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.1109/PDCAT.2011.56
Parallel and Distributed Computing, Applications and Technologies, PDCAT Proceedings, p. 269-274.
http://hdl.handle.net/11449/72859
10.1109/PDCAT.2011.56
2-s2.0-84856658056
4644812253875832
5914651754517864
0000-0002-9325-3159
0000-0002-7449-9022
url http://dx.doi.org/10.1109/PDCAT.2011.56
http://hdl.handle.net/11449/72859
identifier_str_mv Parallel and Distributed Computing, Applications and Technologies, PDCAT Proceedings, p. 269-274.
10.1109/PDCAT.2011.56
2-s2.0-84856658056
4644812253875832
5914651754517864
0000-0002-9325-3159
0000-0002-7449-9022
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Parallel and Distributed Computing, Applications and Technologies, PDCAT Proceedings
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 269-274
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
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