CHSMST plus : An Algorithm for Spatial Clustering
Autor(a) principal: | |
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Data de Publicação: | 2016 |
Outros Autores: | , , , , , , , |
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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Repositório Institucional da UNESP |
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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 |
|
_version_ |
1808129093635407872 |