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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 PublicationPattern Recognition Letters
2018
Volume109Pages:81-88
ISSN0167-8655
Language英语
KeywordGrid Based Clustering Density Based Clustering Dbscan Griden Data Mining Massive Spatial Data Parallel Computing
AbstractDensity-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 BySCI ; EI
Document Type期刊论文
Identifierhttp://ir.nssc.ac.cn/handle/122/6143
Collection空间技术部
Corresponding AuthorSun, 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.
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