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.2018-11-282018-11-282016-01-012016 17th International Conference On Parallel And Distributed Computing, Applications And Technologies (pdcat). New York: Ieee, p. 352-357, 2016.http://hdl.handle.net/11449/165635Spatial 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.352-357engSpatial Data MiningSpatial ClusteringHyper Surface Classification (HSC)Minimum Spanning Tree (MST)CHSMST (Clustering based on Hyper Surface and Minimum Spanning Tree)Trabalho apresentado em evento10.1109/PDCAT.2016.80WOS:000403774200071Acesso aberto464481225387583221390538148793120000-0002-9325-3159