Mining negative rules: a literature review focusing on performance
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , , , , , |
Tipo de documento: | Artigo de conferência |
Idioma: | por |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://hdl.handle.net/11449/237784 |
Resumo: | Mining of frequent patterns and association rules is a Data Mining task that aims to determine consistent relationships among elements in a transaction database. Algorithms that consider the absence of elements perform the generation of so-called negative rules which result in associations of great interest for some applications, enabling it to obtain extra knowledge in comparison to the positive case. This type of association presents a problem regarding the increased amount of generated rules which demands adequate computational resources. This study presents a systematic review with the aim of grouping the concepts of the main contemporary works on this topic, in order to assist the development of future works in this subject. |
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Mining negative rules: a literature review focusing on performanceData miningFrequent patternsNegative association rulesParallel algorithmsSystematic literature reviewMining of frequent patterns and association rules is a Data Mining task that aims to determine consistent relationships among elements in a transaction database. Algorithms that consider the absence of elements perform the generation of so-called negative rules which result in associations of great interest for some applications, enabling it to obtain extra knowledge in comparison to the positive case. This type of association presents a problem regarding the increased amount of generated rules which demands adequate computational resources. This study presents a systematic review with the aim of grouping the concepts of the main contemporary works on this topic, in order to assist the development of future works in this subject.Univ Estadual Paulista, Dept Comp, Bauru, SP, BrazilUniv Estadual Paulista, Dept Ciencias Comp & Estat, Sao Jose Do Rio Preto, SP, BrazilHumber Inst Technol & Adv Learning, Fac Business, Toronto, ON, CanadaUniv Estadual Paulista, Dept Comp, Bauru, SP, BrazilUniv Estadual Paulista, Dept Ciencias Comp & Estat, Sao Jose Do Rio Preto, SP, BrazilIeeeUniversidade Estadual Paulista (UNESP)Humber Inst Technol & Adv LearningColombo, Alexandre [UNESP]Spolon, Roberta [UNESP]Lobato, Renata Spolon [UNESP]Manacero Junior, Aleardo [UNESP]Cavenaghi, Marcos AntonioRocha, A.Goncalves, R.Penalvo, F. G.Martins, J.2022-11-30T13:44:54Z2022-11-30T13:44:54Z2021-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject6Proceedings Of 2021 16th Iberian Conference On Information Systems And Technologies (cisti'2021). New York: Ieee, 6 p., 2021.2166-0727http://hdl.handle.net/11449/237784WOS:000824588500284Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPporProceedings Of 2021 16th Iberian Conference On Information Systems And Technologies (cisti'2021)info:eu-repo/semantics/openAccess2024-04-23T16:11:33Zoai:repositorio.unesp.br:11449/237784Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T21:31:39.836378Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Mining negative rules: a literature review focusing on performance |
title |
Mining negative rules: a literature review focusing on performance |
spellingShingle |
Mining negative rules: a literature review focusing on performance Colombo, Alexandre [UNESP] Data mining Frequent patterns Negative association rules Parallel algorithms Systematic literature review |
title_short |
Mining negative rules: a literature review focusing on performance |
title_full |
Mining negative rules: a literature review focusing on performance |
title_fullStr |
Mining negative rules: a literature review focusing on performance |
title_full_unstemmed |
Mining negative rules: a literature review focusing on performance |
title_sort |
Mining negative rules: a literature review focusing on performance |
author |
Colombo, Alexandre [UNESP] |
author_facet |
Colombo, Alexandre [UNESP] Spolon, Roberta [UNESP] Lobato, Renata Spolon [UNESP] Manacero Junior, Aleardo [UNESP] Cavenaghi, Marcos Antonio Rocha, A. Goncalves, R. Penalvo, F. G. Martins, J. |
author_role |
author |
author2 |
Spolon, Roberta [UNESP] Lobato, Renata Spolon [UNESP] Manacero Junior, Aleardo [UNESP] Cavenaghi, Marcos Antonio Rocha, A. Goncalves, R. Penalvo, F. G. Martins, J. |
author2_role |
author author author author author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (UNESP) Humber Inst Technol & Adv Learning |
dc.contributor.author.fl_str_mv |
Colombo, Alexandre [UNESP] Spolon, Roberta [UNESP] Lobato, Renata Spolon [UNESP] Manacero Junior, Aleardo [UNESP] Cavenaghi, Marcos Antonio Rocha, A. Goncalves, R. Penalvo, F. G. Martins, J. |
dc.subject.por.fl_str_mv |
Data mining Frequent patterns Negative association rules Parallel algorithms Systematic literature review |
topic |
Data mining Frequent patterns Negative association rules Parallel algorithms Systematic literature review |
description |
Mining of frequent patterns and association rules is a Data Mining task that aims to determine consistent relationships among elements in a transaction database. Algorithms that consider the absence of elements perform the generation of so-called negative rules which result in associations of great interest for some applications, enabling it to obtain extra knowledge in comparison to the positive case. This type of association presents a problem regarding the increased amount of generated rules which demands adequate computational resources. This study presents a systematic review with the aim of grouping the concepts of the main contemporary works on this topic, in order to assist the development of future works in this subject. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-01-01 2022-11-30T13:44:54Z 2022-11-30T13:44:54Z |
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 |
Proceedings Of 2021 16th Iberian Conference On Information Systems And Technologies (cisti'2021). New York: Ieee, 6 p., 2021. 2166-0727 http://hdl.handle.net/11449/237784 WOS:000824588500284 |
identifier_str_mv |
Proceedings Of 2021 16th Iberian Conference On Information Systems And Technologies (cisti'2021). New York: Ieee, 6 p., 2021. 2166-0727 WOS:000824588500284 |
url |
http://hdl.handle.net/11449/237784 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
Proceedings Of 2021 16th Iberian Conference On Information Systems And Technologies (cisti'2021) |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
6 |
dc.publisher.none.fl_str_mv |
Ieee |
publisher.none.fl_str_mv |
Ieee |
dc.source.none.fl_str_mv |
Web of Science 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) |
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1808129331042451456 |