An hybrid architecture for clusters analysis: rough setstheory and self-organizing map artificial neural network

Detalhes bibliográficos
Autor(a) principal: Sassi,Renato José
Data de Publicação: 2012
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Pesquisa operacional (Online)
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-74382012000100009
Resumo: The database of real world contains a huge volume of data and among them there are hidden piles of interesting relations that are actually very hard to find out. The knowledge discovery in databases (KDD) appears as a possible solution to find out such relations aiming at converting information into knowledge. However, not all data presented in the bases are useful to a KDD. Usually, data are processed before being presented to a KDD aiming at reducing the amount of data and also at selecting more relevant data to be used by the system. This work consists in the use of Rough Sets Theory, in order to pre-processing data to be presented to Self-Organizing Map neural network (Hybrid Architecture) for clusters analysis. Experiments' results evidence the better performance using the Hybrid Architecture than Self-Organizing Map. The paper also presents all phases of the KDD process.
id SOBRAPO-1_cb0a33967ff49a3df07457b41fe81968
oai_identifier_str oai:scielo:S0101-74382012000100009
network_acronym_str SOBRAPO-1
network_name_str Pesquisa operacional (Online)
repository_id_str
spelling An hybrid architecture for clusters analysis: rough setstheory and self-organizing map artificial neural networkclusters analysisrough sets theoryself-organizing mapThe database of real world contains a huge volume of data and among them there are hidden piles of interesting relations that are actually very hard to find out. The knowledge discovery in databases (KDD) appears as a possible solution to find out such relations aiming at converting information into knowledge. However, not all data presented in the bases are useful to a KDD. Usually, data are processed before being presented to a KDD aiming at reducing the amount of data and also at selecting more relevant data to be used by the system. This work consists in the use of Rough Sets Theory, in order to pre-processing data to be presented to Self-Organizing Map neural network (Hybrid Architecture) for clusters analysis. Experiments' results evidence the better performance using the Hybrid Architecture than Self-Organizing Map. The paper also presents all phases of the KDD process.Sociedade Brasileira de Pesquisa Operacional2012-04-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-74382012000100009Pesquisa Operacional v.32 n.1 2012reponame:Pesquisa operacional (Online)instname:Sociedade Brasileira de Pesquisa Operacional (SOBRAPO)instacron:SOBRAPO10.1590/S0101-74382012005000001info:eu-repo/semantics/openAccessSassi,Renato Joséeng2012-05-02T00:00:00Zoai:scielo:S0101-74382012000100009Revistahttp://www.scielo.br/popehttps://old.scielo.br/oai/scielo-oai.php||sobrapo@sobrapo.org.br1678-51420101-7438opendoar:2012-05-02T00:00Pesquisa operacional (Online) - Sociedade Brasileira de Pesquisa Operacional (SOBRAPO)false
dc.title.none.fl_str_mv An hybrid architecture for clusters analysis: rough setstheory and self-organizing map artificial neural network
title An hybrid architecture for clusters analysis: rough setstheory and self-organizing map artificial neural network
spellingShingle An hybrid architecture for clusters analysis: rough setstheory and self-organizing map artificial neural network
Sassi,Renato José
clusters analysis
rough sets theory
self-organizing map
title_short An hybrid architecture for clusters analysis: rough setstheory and self-organizing map artificial neural network
title_full An hybrid architecture for clusters analysis: rough setstheory and self-organizing map artificial neural network
title_fullStr An hybrid architecture for clusters analysis: rough setstheory and self-organizing map artificial neural network
title_full_unstemmed An hybrid architecture for clusters analysis: rough setstheory and self-organizing map artificial neural network
title_sort An hybrid architecture for clusters analysis: rough setstheory and self-organizing map artificial neural network
author Sassi,Renato José
author_facet Sassi,Renato José
author_role author
dc.contributor.author.fl_str_mv Sassi,Renato José
dc.subject.por.fl_str_mv clusters analysis
rough sets theory
self-organizing map
topic clusters analysis
rough sets theory
self-organizing map
description The database of real world contains a huge volume of data and among them there are hidden piles of interesting relations that are actually very hard to find out. The knowledge discovery in databases (KDD) appears as a possible solution to find out such relations aiming at converting information into knowledge. However, not all data presented in the bases are useful to a KDD. Usually, data are processed before being presented to a KDD aiming at reducing the amount of data and also at selecting more relevant data to be used by the system. This work consists in the use of Rough Sets Theory, in order to pre-processing data to be presented to Self-Organizing Map neural network (Hybrid Architecture) for clusters analysis. Experiments' results evidence the better performance using the Hybrid Architecture than Self-Organizing Map. The paper also presents all phases of the KDD process.
publishDate 2012
dc.date.none.fl_str_mv 2012-04-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-74382012000100009
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-74382012000100009
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/S0101-74382012005000001
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv text/html
dc.publisher.none.fl_str_mv Sociedade Brasileira de Pesquisa Operacional
publisher.none.fl_str_mv Sociedade Brasileira de Pesquisa Operacional
dc.source.none.fl_str_mv Pesquisa Operacional v.32 n.1 2012
reponame:Pesquisa operacional (Online)
instname:Sociedade Brasileira de Pesquisa Operacional (SOBRAPO)
instacron:SOBRAPO
instname_str Sociedade Brasileira de Pesquisa Operacional (SOBRAPO)
instacron_str SOBRAPO
institution SOBRAPO
reponame_str Pesquisa operacional (Online)
collection Pesquisa operacional (Online)
repository.name.fl_str_mv Pesquisa operacional (Online) - Sociedade Brasileira de Pesquisa Operacional (SOBRAPO)
repository.mail.fl_str_mv ||sobrapo@sobrapo.org.br
_version_ 1750318017395294208

Registros relacionados