CHSMST plus : An Algorithm for Spatial Clustering

• Autor(a) principal: Valencio, Carlos Roberto [UNESP]
• Data de Publicação: 2016
• Outros Autores: 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.
• Tipo de documento: Artigo de conferência
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1109/PDCAT.2016.80; http://hdl.handle.net/11449/165635
• topic: Spatial Data Mining; Spatial Clustering; Hyper Surface Classification (HSC); Minimum Spanning Tree (MST); CHSMST (Clustering based on Hyper Surface and Minimum Spanning Tree)

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.