CHSMST plus : An Algorithm for Spatial Clustering

dc.contributorUniversidade Estadual Paulista (Unesp)
dc.contributorUniversidade Federal de São Carlos (UFSCar)
dc.date.accessioned2018-11-28T13:17:40Z
dc.date.available2018-11-28T13:17:40Z
dc.date.created2018-11-28T13:17:40Z
dc.date.issued2016-01-01
dc.identifier2016 17th International Conference On Parallel And Distributed Computing, Applications And Technologies (pdcat). New York: Ieee, p. 352-357, 2016.
dc.identifierhttp://hdl.handle.net/11449/165635
dc.identifier10.1109/PDCAT.2016.80
dc.identifierWOS:000403774200071
dc.identifier4644812253875832
dc.identifier2139053814879312
dc.identifier0000-0002-9325-3159
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.
dc.languageeng
dc.publisherIeee
dc.relation2016 17th International Conference On Parallel And Distributed Computing, Applications And Technologies (pdcat)
dc.rightsAcesso 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 Clustering
dc.typeActas de congresos


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