| GRIDEN: An effective grid-based and density-based spatial clustering algorithm to support parallel computing | |
| Deng, Chao; Song, Jinwei; Sun, Ruizhi; Cai, Saihua; Shi, Yinxue; Sun, Ruizhi (sunruizhi@cau.edu.cn) | |
| Department | 空间技术部 |
| Source Publication | Pattern Recognition Letters
 |
| 2018 | |
| Volume | 109Pages:81-88 |
| ISSN | 0167-8655 |
| Language | 英语 |
| Keyword | Grid Based Clustering Density Based Clustering Dbscan Griden Data Mining Massive Spatial Data Parallel Computing |
| Abstract | 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. |
| Indexed By | SCI ; EI |
| Document Type | 期刊论文 |
| Identifier | http://ir.nssc.ac.cn/handle/122/6143 |
| Collection | 空间技术部 |
| Corresponding Author | Sun, Ruizhi (sunruizhi@cau.edu.cn) |
| Recommended Citation GB/T 7714 | 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. |
| APA | Deng, Chao,Song, Jinwei,Sun, Ruizhi,Cai, Saihua,Shi, Yinxue,&Sun, Ruizhi .(2018).GRIDEN: An effective grid-based and density-based spatial clustering algorithm to support parallel computing.Pattern Recognition Letters,109,81-88. |
| MLA | Deng, Chao,et al."GRIDEN: An effective grid-based and density-based spatial clustering algorithm to support parallel computing".Pattern Recognition Letters 109(2018):81-88. |
| Files in This Item: | ||||||
| File Name/Size | DocType | Version | Access | License | ||
| 2017S016786551730417（2429KB） | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | Application Full Text | |
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