| 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 Publication | International Journal of Simulation and Process Modelling
 |
| 2018 | |
| Volume | 13Issue:5Pages:496-505 |
| DOI | 10.1504/IJSPM.2018.094740 |
| ISSN | 1740-2123 |
| Language | 英语 |
| Abstract | 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. |
| Indexed By | EI |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.nssc.ac.cn/handle/122/6603 |
| Collection | 空间技术部 |
| Affiliation | 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 |
| 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. |
| Files in This Item: | Download All | |||||
| File Name/Size | DocType | Version | Access | License | ||
| 2018135496-505.pdf（485KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View Download | |
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