| 题名: | GRIDEN: An effective grid-based and density-based spatial clustering algorithm to support parallel computing |
| 作者: | Deng, Chao; Song, Jinwei; Sun, Ruizhi; Cai, Saihua; Shi, Yinxue
|
| 作者部门: | 空间技术部
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| 通讯作者: | Sun, Ruizhi (sunruizhi@cau.edu.cn)
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| 关键词: | Grid based clustering
; Density based clustering
; DBSCAN
; GRIDEN
; Data mining
; Massive spatial data
; Parallel computing
|
| 刊名: | Pattern Recognition Letters
|
| ISSN号: | 0167-8655
|
| 出版日期: | 2018
|
| 卷号: | 109, 页码:81-88 | | 收录类别: | SCI
; EI
|
| 项目资助者: | Chinese Universities Scientific Fund [2017XD001]
; China Tobacco Guangxi Industrial Co., Ltd.
|
| 英文摘要: | Density-based clustering has been widely used in many fields. A new effective grid-based and density-based spatial clustering algorithm, GRIDEN, is proposed in this paper, which supports parallel computing in addition to multi-density clustering. It constructs grids using hyper-square cells and provides users with parameter k to control the balance between efficiency and accuracy to increase the flexibility of the algorithm. Compared with conventional density-based algorithms, it achieves much higher performance by eliminating distance calculations among points based on the newly proposed concept of Ε-neighbor cells. Compared with conventional grid-based algorithms, it uses a set of symmetric (2k+1)D cells to identify dense cells and the density-connected relationships among cells. Therefore, the maximum calculated deviation of Ε-neighbor points in the grid-based algorithm can be controlled to an acceptable level through parameter k. In our experiments, the results demonstrate that GRIDEN can achieve a reliable clustering result that is infinite closed with respect to the exact DBSCAN as parameter k grows, and it requires computational time that is only linear to N. © 2017 Elsevier B.V. |
| 语种: | 英语
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| 内容类型: | 期刊论文
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| URI标识: | http://ir.nssc.ac.cn/handle/122/6143
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| Appears in Collections: | 空间技术部_期刊论文
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| 2017S0167865517304178.pdf（2429KB） | 期刊论文 | 作者接受稿 | 限制开放 | | View
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| Recommended Citation: | |
Deng, Chao,Song, Jinwei,Sun, Ruizhi,et al. GRIDEN: An effective grid-based and density-based spatial clustering algorithm to support parallel computing[J]. Pattern Recognition Letters,2018,109:81-88.
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