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A comparative study of two optimization clustering techniques on unemployment data

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Resumo:An important strategy for data classification consists in organising data points in clusters. The $k$-means is a traditional optimisation method applied to cluster data points. Using a labour market database, we suggest the application of an alternative method based on the computation of the dominant eigenvalue of a matrix related with the distance among data points. This approach presents results consistent with the results obtained by the k-means.
Autores principais:Barros, Elisa
Outros Autores:Nunes, Alcina; Balsa, Carlos
Assunto:Clustering methods k-means Spectral clustering Unemployment data mining
Ano:2013
País:Portugal
Tipo de documento:documento de conferência
Tipo de acesso:acesso aberto
Instituição associada:Instituto Politécnico de Bragança
Idioma:português
Origem:Biblioteca Digital do IPB
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author Barros, Elisa
author2 Nunes, Alcina
Balsa, Carlos
author2_role author
author
author_facet Barros, Elisa
Nunes, Alcina
Balsa, Carlos
author_role author
contributor_name_str_mv Biblioteca Digital do IPB
country_str PT
creators_json_txt [{\"Person.name\":\"Barros, Elisa\",\"Person.identifier.orcid\":\"0000-0001-8515-695X\"},{\"Person.name\":\"Nunes, Alcina\",\"Person.identifier.orcid\":\"0000-0003-4056-9747\"},{\"Person.name\":\"Balsa, Carlos\",\"Person.identifier.orcid\":\"0000-0003-2431-8665\"}]
datacite.contributors.contributor.contributorName.fl_str_mv Biblioteca Digital do IPB
datacite.creators.creator.creatorName.fl_str_mv Barros, Elisa
Nunes, Alcina
Balsa, Carlos
datacite.date.Accepted.fl_str_mv 2013-01-01T00:00:00Z
datacite.date.available.fl_str_mv 2014-09-10T12:48:21Z
datacite.date.embargoed.fl_str_mv 2014-09-10T12:48:21Z
datacite.rights.fl_str_mv http://purl.org/coar/access_right/c_abf2
datacite.subjects.subject.fl_str_mv Clustering methods
k-means
Spectral clustering
Unemployment data mining
datacite.titles.title.fl_str_mv A comparative study of two optimization clustering techniques on unemployment data
dc.contributor.none.fl_str_mv Biblioteca Digital do IPB
dc.creator.none.fl_str_mv Barros, Elisa
Nunes, Alcina
Balsa, Carlos
dc.date.Accepted.fl_str_mv 2013-01-01T00:00:00Z
dc.date.available.fl_str_mv 2014-09-10T12:48:21Z
dc.date.embargoed.fl_str_mv 2014-09-10T12:48:21Z
dc.format.none.fl_str_mv application/pdf
dc.identifier.none.fl_str_mv http://hdl.handle.net/10198/10378
dc.language.none.fl_str_mv por
dc.publisher.none.fl_str_mv Instituto Politécnico de Bragança
dc.rights.none.fl_str_mv http://purl.org/coar/access_right/c_abf2
dc.subject.none.fl_str_mv Clustering methods
k-means
Spectral clustering
Unemployment data mining
dc.title.fl_str_mv A comparative study of two optimization clustering techniques on unemployment data
dc.type.none.fl_str_mv http://purl.org/coar/resource_type/c_c94f
description An important strategy for data classification consists in organising data points in clusters. The $k$-means is a traditional optimisation method applied to cluster data points. Using a labour market database, we suggest the application of an alternative method based on the computation of the dominant eigenvalue of a matrix related with the distance among data points. This approach presents results consistent with the results obtained by the k-means.
dirty 0
eu_rights_str_mv openAccess
format conferenceObject
fulltext.url.fl_str_mv https://bibliotecadigital.ipb.pt/bitstreams/7d4cb869-e9fa-4fd4-821a-4700b6327714/download
id ipb_5029bc02f6c72d274cd6ca878a2e57ee
identifier.url.fl_str_mv http://hdl.handle.net/10198/10378
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institution Instituto Politécnico de Bragança
instname_str Instituto Politécnico de Bragança
language por
network_acronym_str ipb
network_name_str Biblioteca Digital do IPB
oai_identifier_str oai:bibliotecadigital.ipb.pt:10198/10378
organization_str_mv urn:organizationAcronym:ipb
person_str_mv Barros, Elisa
Barros, Elisa
http://orcid.org/0000-0001-8515-695X
0000-0001-8515-695X
Nunes, Alcina
Nunes, Alcina
https://www.ciencia-id.pt/1111-680F-0CAF
1111-680F-0CAF
http://orcid.org/0000-0003-4056-9747
0000-0003-4056-9747
Balsa, Carlos
Balsa, Carlos
https://www.ciencia-id.pt/DE1E-2F7A-AAB1
DE1E-2F7A-AAB1
http://orcid.org/0000-0003-2431-8665
0000-0003-2431-8665
publishDate 2013
publisher.none.fl_str_mv Instituto Politécnico de Bragança
reponame_str Biblioteca Digital do IPB
repository_id_str urn:repositoryAcronym:ipb
service_str_mv urn:repositoryAcronym:ipb
spelling porInstituto Politécnico de BragançaporAn important strategy for data classification consists in organising data points in clusters. The $k$-means is a traditional optimisation method applied to cluster data points. Using a labour market database, we suggest the application of an alternative method based on the computation of the dominant eigenvalue of a matrix related with the distance among data points. This approach presents results consistent with the results obtained by the k-means.application/pdfporA comparative study of two optimization clustering techniques on unemployment dataPersonalBarros, ElisaDSpacehttp://dspace.org/items/29601d32-5c12-4b5f-84ec-55d83617d04eDSpacehttp://dspace.org/items/29601d32-5c12-4b5f-84ec-55d83617d04eBarrosElisaORCIDhttp://orcid.org0000-0001-8515-695XPersonalNunes, AlcinaDSpacehttp://dspace.org/items/f96c3560-c1d3-432c-aa84-49982ea86106DSpacehttp://dspace.org/items/f96c3560-c1d3-432c-aa84-49982ea86106NunesAlcinaCiência IDhttps://www.ciencia-id.pt1111-680F-0CAFORCIDhttp://orcid.org0000-0003-4056-9747Researcher IDhttps://www.researcherid.comM-8259-2013Scopus Author IDhttps://www.scopus.com55907654000PersonalBalsa, CarlosDSpacehttp://dspace.org/items/d0e5ccff-9696-4f4f-9567-8d698a6bf17dDSpacehttp://dspace.org/items/d0e5ccff-9696-4f4f-9567-8d698a6bf17dBalsaCarlosCiência IDhttps://www.ciencia-id.ptDE1E-2F7A-AAB1ORCIDhttp://orcid.org0000-0003-2431-8665Researcher IDhttps://www.researcherid.comM-8735-2013Scopus Author IDhttps://www.scopus.com23391719100HostingInstitutionOrganizationalBiblioteca Digital do IPBe-mailmailto:dspace@ipb.ptdspace@ipb.ptISBNIsPartOf979-972-745-153-12014-09-10T12:48:21Z20132013-01-01T00:00:00ZHandlehttp://hdl.handle.net/10198/10378http://purl.org/coar/access_right/c_abf2open accessClustering methodsk-meansSpectral clusteringUnemployment data mining680921 bytesother research producthttp://purl.org/coar/resource_type/c_c94fconference objecthttp://purl.org/coar/access_right/c_abf2application/pdffulltexthttps://bibliotecadigital.ipb.pt/bitstreams/7d4cb869-e9fa-4fd4-821a-4700b6327714/downloadXVI Congresso da Associação Portuguesa de Investigação Operacional4747Bragança
spellingShingle A comparative study of two optimization clustering techniques on unemployment data
Barros, Elisa
Clustering methods
k-means
Spectral clustering
Unemployment data mining
status SINGLETON
subject.fl_str_mv Clustering methods
k-means
Spectral clustering
Unemployment data mining
title A comparative study of two optimization clustering techniques on unemployment data
title_full A comparative study of two optimization clustering techniques on unemployment data
title_fullStr A comparative study of two optimization clustering techniques on unemployment data
title_full_unstemmed A comparative study of two optimization clustering techniques on unemployment data
title_short A comparative study of two optimization clustering techniques on unemployment data
title_sort A comparative study of two optimization clustering techniques on unemployment data
topic Clustering methods
k-means
Spectral clustering
Unemployment data mining
topic_facet Clustering methods
k-means
Spectral clustering
Unemployment data mining
url http://hdl.handle.net/10198/10378
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