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GRIDEN: An effective grid-based and density-based spatial clustering algorithm to support parallel computing.

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Title: GRIDEN: An effective grid-based and density-based spatial clustering algorithm to support parallel computing.
Authors: Deng, Chao1,2, Song, Jinwei3, Sun, Ruizhi1 sunruizhi@cau.edu.cn, Cai, Saihua1, Shi, Yinxue1
Source: Pattern Recognition Letters. Jul2018, Vol. 109, p81-88. 8p.
Subjects: Digital image processing, Image segmentation, Density functional theory, Computational intelligence, Cyclic loads
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 ( 2 k + 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 . [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
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 ( 2 k + 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 . [ABSTRACT FROM AUTHOR]
ISSN:01678655
DOI:10.1016/j.patrec.2017.11.011