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K-DBSCAN: An efficient density-based clustering algorithm supports parallel computing
Deng, Chao1,2; Song, Jinwei3; Cai, Saihua1; Sun, Ruizhi1; Shi, Yinxue1; Hao, Shangbo1
Department空间技术部
Source PublicationInternational Journal of Simulation and Process Modelling
2018
Volume13Issue:5Pages:496-505
DOI10.1504/IJSPM.2018.094740
ISSN1740-2123
Language英语
AbstractDBSCAN 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.
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Document Type期刊论文
Identifierhttp://ir.nssc.ac.cn/handle/122/6603
Collection空间技术部
Affiliation1.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
Recommended Citation
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.
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