K-DBSCAN: An efficient density-based clustering algorithm supports parallel computing

被引：0
|
作者：
Deng, Chao [1 ,2 ]
Song, Jinwei [3 ]
Cai, Saihua [1 ]
Sun, Ruizhi [1 ]
Shi, Yinxue [1 ]
Hao, Shangbo [1 ]
机构：
[1] College of Information and Electrical Engineering, China Agricultural University, Beijing,100083, China
[2] China Tobacco Guangxi Industrial Co., Ltd., No. 28 Beihunanlu, Xixiangtang District, Nanning,530001, China
[3] National Space Science Center of CAS, No. 1 Nanertiao, Zhongguancun, Haidian district, Beijing,100190, China
来源：
International Journal of Simulation and Process Modelling | 2018年 / 13卷 / 05期
关键词：
K-means clustering;
D O I：
10.1504/IJSPM.2018.094740
中图分类号：
学科分类号：
摘要：
引用
收藏
页码：496 / 505
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