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

doi:10.1504/IJSPM.2018.094740

	K-DBSCAN: An efficient density-based clustering algorithm supports parallel computing
	Deng, Chao 1,2; Song, Jinwei 3; Cai, Saihua 1; Sun, Ruizhi 1; Shi, Yinxue 1; Hao, Shangbo 1
刊名	International Journal of Simulation and Process Modelling
	2018
卷号	13 期号:5 页码:496-505
ISSN号	1740-2123
DOI	10.1504/IJSPM.2018.094740
英文摘要	DBSCAN is the most representative density-based clustering algorithm and has been widely used in many fields. However, the running time of DBSCAN is unacceptable in many actual applications. To improve its performance, this paper presents a new 2D density-based clustering algorithm, K-DBSCAN, which successfully reduces the computational complexity of the clustering process by a simplified k-mean partitioning process and a reachable partition index, and enables parallel computing by a divide-and-conquer method. The experiments show that K-DBSCAN achieves remarkable accuracy, efficiency and applicability compared with conventional DBSCAN algorithms especially in large-scale spatial density-based clustering. The time complexity of K-DBSCAN is O(N2/KC), where K is the number of data partitions, and C is the number of physical computing cores. © 2018 Inderscience Enterprises Ltd.
语种	英语
内容类型	期刊论文
源URL	[http://ir.nssc.ac.cn/handle/122/6603]
专题	国家空间科学中心_空间技术部
作者单位	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
推荐引用方式 GB/T 7714	Deng, Chao,Song, Jinwei,Cai, Saihua,et al. K-DBSCAN: An efficient density-based clustering algorithm supports parallel computing[J]. International Journal of Simulation and Process Modelling,2018,13(5):496-505.
APA	Deng, Chao,Song, Jinwei,Cai, Saihua,Sun, Ruizhi,Shi, Yinxue,&Hao, Shangbo.(2018).K-DBSCAN: An efficient density-based clustering algorithm supports parallel computing.International Journal of Simulation and Process Modelling,13(5),496-505.
MLA	Deng, Chao,et al."K-DBSCAN: An efficient density-based clustering algorithm supports parallel computing".International Journal of Simulation and Process Modelling 13.5(2018):496-505.