CHSMST plus : An Algorithm for Spatial Clustering
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Data
2016-01-01
Autores
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.
Título da Revista
ISSN da Revista
Título de Volume
Editor
Ieee
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.
Descrição
Palavras-chave
Spatial Data Mining, Spatial Clustering, Hyper Surface Classification (HSC), Minimum Spanning Tree (MST), CHSMST (Clustering based on Hyper Surface and Minimum Spanning Tree)
Como citar
2016 17th International Conference On Parallel And Distributed Computing, Applications And Technologies (pdcat). New York: Ieee, p. 352-357, 2016.