CHSMST plus : An Algorithm for Spatial Clustering

Detalhes bibliográficos
Autor(a) principal: Valencio, Carlos Roberto [UNESP]
Data de Publicação: 2016
Outros Autores: Medeiros, Camila Alves [UNESP], Neves, Leandro Alves [UNESP], Donega Zafalon, Geraldo Francisco [UNESP], Gratao de Souza, Rogeria Cristiane [UNESP], Colombini, Angelo Cesar, Shen, H., Sang, Y., Tian, H.
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1109/PDCAT.2016.80
http://hdl.handle.net/11449/165635
Resumo: Spatial clustering has been widely studied due to its application in several areas. However, the algorithms of such technique still need to overcome several challenges to achieve satisfactory results on a timely basis. This work presents an algorithm for spatial clustering based on CHSMST, which allows: data clustering considering both distance and similarity, enabling to correlate spatial and non-spatial data; user interaction is not necessary; and use of multithreading technique to improve the performance. The algorithm was tested is a real database of health area.
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spelling CHSMST plus : An Algorithm for Spatial ClusteringSpatial Data MiningSpatial ClusteringHyper Surface Classification (HSC)Minimum Spanning Tree (MST)CHSMST (Clustering based on Hyper Surface and Minimum Spanning Tree)Spatial clustering has been widely studied due to its application in several areas. However, the algorithms of such technique still need to overcome several challenges to achieve satisfactory results on a timely basis. This work presents an algorithm for spatial clustering based on CHSMST, which allows: data clustering considering both distance and similarity, enabling to correlate spatial and non-spatial data; user interaction is not necessary; and use of multithreading technique to improve the performance. The algorithm was tested is a real database of health area.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Sao Paulo State Univ UNESP, Dept Comp Sci & Stat, Sao Paulo, BrazilFed Univ Sao Carlos UFSCar, Dept Comp Sci & Stat, Sao Carlos, SP, BrazilSao Paulo State Univ UNESP, Dept Comp Sci & Stat, Sao Paulo, BrazilIeeeUniversidade Estadual Paulista (Unesp)Universidade Federal de São Carlos (UFSCar)Valencio, Carlos Roberto [UNESP]Medeiros, Camila Alves [UNESP]Neves, Leandro Alves [UNESP]Donega Zafalon, Geraldo Francisco [UNESP]Gratao de Souza, Rogeria Cristiane [UNESP]Colombini, Angelo CesarShen, H.Sang, Y.Tian, H.2018-11-28T13:17:40Z2018-11-28T13:17:40Z2016-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject352-357http://dx.doi.org/10.1109/PDCAT.2016.802016 17th International Conference On Parallel And Distributed Computing, Applications And Technologies (pdcat). New York: Ieee, p. 352-357, 2016.http://hdl.handle.net/11449/16563510.1109/PDCAT.2016.80WOS:000403774200071464481225387583221390538148793120000-0002-9325-3159Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPeng2016 17th International Conference On Parallel And Distributed Computing, Applications And Technologies (pdcat)info:eu-repo/semantics/openAccess2021-10-23T21:47:03Zoai:repositorio.unesp.br:11449/165635Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T19:35:58.308150Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv CHSMST plus : An Algorithm for Spatial Clustering
title CHSMST plus : An Algorithm for Spatial Clustering
spellingShingle CHSMST plus : An Algorithm for Spatial Clustering
Valencio, Carlos Roberto [UNESP]
Spatial Data Mining
Spatial Clustering
Hyper Surface Classification (HSC)
Minimum Spanning Tree (MST)
CHSMST (Clustering based on Hyper Surface and Minimum Spanning Tree)
title_short CHSMST plus : An Algorithm for Spatial Clustering
title_full CHSMST plus : An Algorithm for Spatial Clustering
title_fullStr CHSMST plus : An Algorithm for Spatial Clustering
title_full_unstemmed CHSMST plus : An Algorithm for Spatial Clustering
title_sort CHSMST plus : An Algorithm for Spatial Clustering
author Valencio, Carlos Roberto [UNESP]
author_facet Valencio, Carlos Roberto [UNESP]
Medeiros, Camila Alves [UNESP]
Neves, Leandro Alves [UNESP]
Donega Zafalon, Geraldo Francisco [UNESP]
Gratao de Souza, Rogeria Cristiane [UNESP]
Colombini, Angelo Cesar
Shen, H.
Sang, Y.
Tian, H.
author_role author
author2 Medeiros, Camila Alves [UNESP]
Neves, Leandro Alves [UNESP]
Donega Zafalon, Geraldo Francisco [UNESP]
Gratao de Souza, Rogeria Cristiane [UNESP]
Colombini, Angelo Cesar
Shen, H.
Sang, Y.
Tian, H.
author2_role author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (Unesp)
Universidade Federal de São Carlos (UFSCar)
dc.contributor.author.fl_str_mv Valencio, Carlos Roberto [UNESP]
Medeiros, Camila Alves [UNESP]
Neves, Leandro Alves [UNESP]
Donega Zafalon, Geraldo Francisco [UNESP]
Gratao de Souza, Rogeria Cristiane [UNESP]
Colombini, Angelo Cesar
Shen, H.
Sang, Y.
Tian, H.
dc.subject.por.fl_str_mv Spatial Data Mining
Spatial Clustering
Hyper Surface Classification (HSC)
Minimum Spanning Tree (MST)
CHSMST (Clustering based on Hyper Surface and Minimum Spanning Tree)
topic Spatial Data Mining
Spatial Clustering
Hyper Surface Classification (HSC)
Minimum Spanning Tree (MST)
CHSMST (Clustering based on Hyper Surface and Minimum Spanning Tree)
description Spatial clustering has been widely studied due to its application in several areas. However, the algorithms of such technique still need to overcome several challenges to achieve satisfactory results on a timely basis. This work presents an algorithm for spatial clustering based on CHSMST, which allows: data clustering considering both distance and similarity, enabling to correlate spatial and non-spatial data; user interaction is not necessary; and use of multithreading technique to improve the performance. The algorithm was tested is a real database of health area.
publishDate 2016
dc.date.none.fl_str_mv 2016-01-01
2018-11-28T13:17:40Z
2018-11-28T13:17:40Z
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.2016.80
2016 17th International Conference On Parallel And Distributed Computing, Applications And Technologies (pdcat). New York: Ieee, p. 352-357, 2016.
http://hdl.handle.net/11449/165635
10.1109/PDCAT.2016.80
WOS:000403774200071
4644812253875832
2139053814879312
0000-0002-9325-3159
url http://dx.doi.org/10.1109/PDCAT.2016.80
http://hdl.handle.net/11449/165635
identifier_str_mv 2016 17th International Conference On Parallel And Distributed Computing, Applications And Technologies (pdcat). New York: Ieee, p. 352-357, 2016.
10.1109/PDCAT.2016.80
WOS:000403774200071
4644812253875832
2139053814879312
0000-0002-9325-3159
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2016 17th International Conference On Parallel And Distributed Computing, Applications And Technologies (pdcat)
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 352-357
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)
repository.mail.fl_str_mv
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