GRIDEN: An effective grid-based and density-based spatial clustering algorithm to support parallel computing
•Propose a new effective density-based and grid-based clustering algorithm GRIDEN for massive spatial data.•Present a new concept of ε-neighbor cells to improve the clustering accuracy of grid-based algorithm.•Present a parallel computing algorithm for high dimensional density-based clustering to ac...
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| Published in: | Pattern recognition letters Vol. 109; pp. 81 - 88 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
15.07.2018
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| ISSN: | 0167-8655, 1872-7344 |
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| Abstract | •Propose a new effective density-based and grid-based clustering algorithm GRIDEN for massive spatial data.•Present a new concept of ε-neighbor cells to improve the clustering accuracy of grid-based algorithm.•Present a parallel computing algorithm for high dimensional density-based clustering to achieve high performance.•Supports for multi-density clustering and incremental density-based clustering.
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. |
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| AbstractList | •Propose a new effective density-based and grid-based clustering algorithm GRIDEN for massive spatial data.•Present a new concept of ε-neighbor cells to improve the clustering accuracy of grid-based algorithm.•Present a parallel computing algorithm for high dimensional density-based clustering to achieve high performance.•Supports for multi-density clustering and incremental density-based clustering.
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. 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. |
| Author | Shi, Yinxue Song, Jinwei Deng, Chao Sun, Ruizhi Cai, Saihua |
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| Cites_doi | 10.1016/j.phpro.2012.02.174 10.1049/cje.2016.05.001 10.1007/11811305_29 10.1126/science.1242072 10.1109/TCYB.2015.2403356 |
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| Keywords | Massive spatial data GRIDEN Parallel computing DBSCAN Data mining Density-based clustering Grid-based clustering |
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| SubjectTerms | Algorithms Clustering Computer applications Computing time Data mining DBSCAN Delta cells Density Density-based clustering Grid-based clustering GRIDEN Massive spatial data Mathematical analysis Parallel computing Parallel processing Parameters Spatial data |
| Title | GRIDEN: An effective grid-based and density-based spatial clustering algorithm to support parallel computing |
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