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CHSMST plus : An Algorithm for Spatial Clustering

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dc.contributor.authorValencio, Carlos Roberto [UNESP]
dc.contributor.authorMedeiros, Camila Alves [UNESP]
dc.contributor.authorNeves, Leandro Alves [UNESP]
dc.contributor.authorDonega Zafalon, Geraldo Francisco [UNESP]
dc.contributor.authorGratao de Souza, Rogeria Cristiane [UNESP]
dc.contributor.authorColombini, Angelo Cesar
dc.contributor.authorShen, H.
dc.contributor.authorSang, Y.
dc.contributor.authorTian, H.
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionUniversidade Federal de São Carlos (UFSCar)
dc.date.accessioned2018-11-28T13:17:40Z
dc.date.available2018-11-28T13:17:40Z
dc.date.issued2016-01-01
dc.description.abstractSpatial 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.en
dc.description.affiliationSao Paulo State Univ UNESP, Dept Comp Sci & Stat, Sao Paulo, Brazil
dc.description.affiliationFed Univ Sao Carlos UFSCar, Dept Comp Sci & Stat, Sao Carlos, SP, Brazil
dc.description.affiliationUnespSao Paulo State Univ UNESP, Dept Comp Sci & Stat, Sao Paulo, Brazil
dc.description.sponsorshipCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.format.extent352-357
dc.identifierhttp://dx.doi.org/10.1109/PDCAT.2016.80
dc.identifier.citation2016 17th International Conference On Parallel And Distributed Computing, Applications And Technologies (pdcat). New York: Ieee, p. 352-357, 2016.
dc.identifier.doi10.1109/PDCAT.2016.80
dc.identifier.lattes4644812253875832
dc.identifier.lattes2139053814879312
dc.identifier.orcid0000-0002-9325-3159
dc.identifier.urihttp://hdl.handle.net/11449/165635
dc.identifier.wosWOS:000403774200071
dc.language.isoeng
dc.publisherIeee
dc.relation.ispartof2016 17th International Conference On Parallel And Distributed Computing, Applications And Technologies (pdcat)
dc.rights.accessRightsAcesso aberto
dc.sourceWeb of Science
dc.subjectSpatial Data Mining
dc.subjectSpatial Clustering
dc.subjectHyper Surface Classification (HSC)
dc.subjectMinimum Spanning Tree (MST)
dc.subjectCHSMST (Clustering based on Hyper Surface and Minimum Spanning Tree)
dc.titleCHSMST plus : An Algorithm for Spatial Clusteringen
dc.typeTrabalho apresentado em evento
dcterms.licensehttp://www.ieee.org/publications_standards/publications/rights/rights_policies.html
dcterms.rightsHolderIeee
dspace.entity.typePublication
unesp.author.lattes4644812253875832[1]
unesp.author.lattes2139053814879312
unesp.author.orcid0000-0002-9325-3159[1]
unesp.campusUniversidade Estadual Paulista (UNESP), Instituto de Biociências Letras e Ciências Exatas, São José do Rio Pretopt
unesp.departmentCiências da Computação e Estatística - IBILCEpt

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São José do Rio Preto - IBILCE - Instituto de Biociências, Letras e Ciências Exatas