CHSMST+: 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.authorZafalon, Geraldo Francisco Donega [UNESP]
dc.contributor.authorDe Souza, Rogeria Cristiane Gratao [UNESP]
dc.contributor.authorColombini, Angelo Cesar
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionUniversidade Federal de São Carlos (UFSCar)
dc.date.accessioned2018-12-11T17:33:04Z
dc.date.available2018-12-11T17:33:04Z
dc.date.issued2017-06-07
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 nonspatial data, user interaction is not necessary, and use of multithreading technique to improve the performance. The algorithm was tested ia a real database of health area.en
dc.description.affiliationDepartment of Computer Science and Statistics São Paulo State University (UNESP)
dc.description.affiliationDepartment of Computer Science and Statistics Federal University of São Carlos (UFSCar)
dc.description.affiliationUnespDepartment of Computer Science and Statistics São Paulo State University (UNESP)
dc.format.extent352-357
dc.identifierhttp://dx.doi.org/10.1109/PDCAT.2016.081
dc.identifier.citationParallel and Distributed Computing, Applications and Technologies, PDCAT Proceedings, p. 352-357.
dc.identifier.doi10.1109/PDCAT.2016.081
dc.identifier.lattes4644812253875832
dc.identifier.lattes2139053814879312
dc.identifier.orcid0000-0002-9325-3159
dc.identifier.scopus2-s2.0-85021874602
dc.identifier.urihttp://hdl.handle.net/11449/178996
dc.language.isoeng
dc.relation.ispartofParallel and Distributed Computing, Applications and Technologies, PDCAT Proceedings
dc.rights.accessRightsAcesso aberto
dc.sourceScopus
dc.subjectCHSMST (Clustering based on Hyper Surface and Minimum Spanning Tree)
dc.subjectHyper Surface Classification (HSC)
dc.subjectMinimum Spanning Tree (MST)
dc.subjectSpatial Clustering
dc.subjectSpatial Data Mining
dc.titleCHSMST+: An algorithm for spatial clusteringen
dc.typeTrabalho apresentado em evento
unesp.author.lattes4644812253875832[1]
unesp.author.lattes2139053814879312
unesp.author.orcid0000-0002-9325-3159[1]

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Artigos - Ciência da Computação e Estatística - IBILCE