A user-driven association rule mining based on templates for multi-relational data
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.3844/jcssp.2018.1475.1487 http://hdl.handle.net/11449/190005 |
Resumo: | Data mining algorithms to find association rules are an important tool to extract knowledge from databases. However, these algorithms produce an enormous amount of rules, many of which could be redundant or irrelevant for a specific decision-making process. Also, the use of previous knowledge and hypothesis are not considered by these algorithms. On the other hand, most existing data mining approaches look for patterns in a single data table, ignoring the relations presented in relational databases. The contribution of this paper is the proposition of a multirelational data mining algorithm based on association rules, called TBMRRadix, which considers previous knowledge and hypothesis through the using of the Templates technique. Applying this approach over two real databases, we were able to reduce the number of generated rules, use the existing knowledge about the data and reduce the waste of computational resources while processing. Our experiments show that the developed algorithm was also able to perform in a multi-relational environment, while the MR-Radix, that does not use Templates technique, was not. |
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Repositório Institucional da UNESP |
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A user-driven association rule mining based on templates for multi-relational dataAssociation rulesData miningKnowledge discovery in databasesMulti-relational data miningTemplatesUser-driven filterData mining algorithms to find association rules are an important tool to extract knowledge from databases. However, these algorithms produce an enormous amount of rules, many of which could be redundant or irrelevant for a specific decision-making process. Also, the use of previous knowledge and hypothesis are not considered by these algorithms. On the other hand, most existing data mining approaches look for patterns in a single data table, ignoring the relations presented in relational databases. The contribution of this paper is the proposition of a multirelational data mining algorithm based on association rules, called TBMRRadix, which considers previous knowledge and hypothesis through the using of the Templates technique. Applying this approach over two real databases, we were able to reduce the number of generated rules, use the existing knowledge about the data and reduce the waste of computational resources while processing. Our experiments show that the developed algorithm was also able to perform in a multi-relational environment, while the MR-Radix, that does not use Templates technique, was not.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)São Paulo State University (Unesp) Institute of Biosciences Humanities and Exact Sciences (Ibilce) Campus São José do Rio PretoFluminense Federal University (UFF)São Paulo University (EESC-USP)São Paulo State University (Unesp) Institute of Biosciences Humanities and Exact Sciences (Ibilce) Campus São José do Rio PretoUniversidade Estadual Paulista (Unesp)Fluminense Federal University (UFF)Universidade de São Paulo (USP)Valêncio, Carlos Roberto [UNESP]Morais, Guilherme Henrique [UNESP]Fortes, Márcio ZambotiColombini, Angelo CesarNeves, Leandro Alves [UNESP]Tronco, Mario LuizTenório, William [UNESP]2019-10-06T16:59:11Z2019-10-06T16:59:11Z2018-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article1475-1487http://dx.doi.org/10.3844/jcssp.2018.1475.1487Journal of Computer Science, v. 14, n. 11, p. 1475-1487, 2018.1549-3636http://hdl.handle.net/11449/19000510.3844/jcssp.2018.1475.14872-s2.0-85059465955Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengJournal of Computer Scienceinfo:eu-repo/semantics/openAccess2021-10-23T05:55:24Zoai:repositorio.unesp.br:11449/190005Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T18:49:32.524959Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
A user-driven association rule mining based on templates for multi-relational data |
title |
A user-driven association rule mining based on templates for multi-relational data |
spellingShingle |
A user-driven association rule mining based on templates for multi-relational data Valêncio, Carlos Roberto [UNESP] Association rules Data mining Knowledge discovery in databases Multi-relational data mining Templates User-driven filter |
title_short |
A user-driven association rule mining based on templates for multi-relational data |
title_full |
A user-driven association rule mining based on templates for multi-relational data |
title_fullStr |
A user-driven association rule mining based on templates for multi-relational data |
title_full_unstemmed |
A user-driven association rule mining based on templates for multi-relational data |
title_sort |
A user-driven association rule mining based on templates for multi-relational data |
author |
Valêncio, Carlos Roberto [UNESP] |
author_facet |
Valêncio, Carlos Roberto [UNESP] Morais, Guilherme Henrique [UNESP] Fortes, Márcio Zamboti Colombini, Angelo Cesar Neves, Leandro Alves [UNESP] Tronco, Mario Luiz Tenório, William [UNESP] |
author_role |
author |
author2 |
Morais, Guilherme Henrique [UNESP] Fortes, Márcio Zamboti Colombini, Angelo Cesar Neves, Leandro Alves [UNESP] Tronco, Mario Luiz Tenório, William [UNESP] |
author2_role |
author author author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) Fluminense Federal University (UFF) Universidade de São Paulo (USP) |
dc.contributor.author.fl_str_mv |
Valêncio, Carlos Roberto [UNESP] Morais, Guilherme Henrique [UNESP] Fortes, Márcio Zamboti Colombini, Angelo Cesar Neves, Leandro Alves [UNESP] Tronco, Mario Luiz Tenório, William [UNESP] |
dc.subject.por.fl_str_mv |
Association rules Data mining Knowledge discovery in databases Multi-relational data mining Templates User-driven filter |
topic |
Association rules Data mining Knowledge discovery in databases Multi-relational data mining Templates User-driven filter |
description |
Data mining algorithms to find association rules are an important tool to extract knowledge from databases. However, these algorithms produce an enormous amount of rules, many of which could be redundant or irrelevant for a specific decision-making process. Also, the use of previous knowledge and hypothesis are not considered by these algorithms. On the other hand, most existing data mining approaches look for patterns in a single data table, ignoring the relations presented in relational databases. The contribution of this paper is the proposition of a multirelational data mining algorithm based on association rules, called TBMRRadix, which considers previous knowledge and hypothesis through the using of the Templates technique. Applying this approach over two real databases, we were able to reduce the number of generated rules, use the existing knowledge about the data and reduce the waste of computational resources while processing. Our experiments show that the developed algorithm was also able to perform in a multi-relational environment, while the MR-Radix, that does not use Templates technique, was not. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-01-01 2019-10-06T16:59:11Z 2019-10-06T16:59:11Z |
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/10.3844/jcssp.2018.1475.1487 Journal of Computer Science, v. 14, n. 11, p. 1475-1487, 2018. 1549-3636 http://hdl.handle.net/11449/190005 10.3844/jcssp.2018.1475.1487 2-s2.0-85059465955 |
url |
http://dx.doi.org/10.3844/jcssp.2018.1475.1487 http://hdl.handle.net/11449/190005 |
identifier_str_mv |
Journal of Computer Science, v. 14, n. 11, p. 1475-1487, 2018. 1549-3636 10.3844/jcssp.2018.1475.1487 2-s2.0-85059465955 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Journal of Computer Science |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
1475-1487 |
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_ |
1808128985625788416 |