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General Information
Prof. Wael Badawy
Department of Computing and Information Systems Umm Al Qura University, Canada
I'm happy to take on the position of editor in chief of IJCTE. We encourage authors to submit papers concerning any branch of computer theory and engineering.
IJCTE 2017 Vol.9(3): 151-155 ISSN: 1793-8201
DOI: 10.7763/IJCTE.2017.V9.1129

GO-DBSCAN: Improvements of DBSCAN Algorithm Based on Grid

Ling Feng, Kejian Liu, Fuxi Tang, and Qingrui Meng
Abstract—Abstract—For it can identify the clusters with any shape and tackle the boundary points effectively, the typical density-based method of DBSCAN was widely applied to the clustering analysis. But the algorithm still has some shortcomings, such as the high time complexity, clustering effect is very dependent on the initial value of the parameter, and the low accuracy of the boundary points tackling processes. This paper put forward GO-DBSCAN, which based on the DBSCAN and OPTICS algorithms. GO-DBSCAN improved the accuracy while processing the boundary points, that cause of it introduced the minimum acceptable distance of OPTICS. In order to reduce the time complexity of clustering processes, it also proposed the method of grid-based query while it retraverse the neighborhood. At the end of this paper, we proved that GO-DBSCAN would perform better both on the accuracy of boundary points processing and time complexity.

Index Terms—Index Terms—Clustering, DBSCAN, OPTICS, grid, boundary point.

Ling Feng, Kejian Liu, and Fuxi Tang are with the Xihua University, Sichuan, China (e-mail: lyn_ling_feng@163.com, liukejian@gmail.com, Fuxi_tang@163.com). Qingrui Meng is with Tibet Feiyue Intelligence Technology CO., Ltd, China (e-mail: 414893358@qq.com).


Cite:Ling Feng, Kejian Liu, Fuxi Tang, and Qingrui Meng, "GO-DBSCAN: Improvements of DBSCAN Algorithm Based on Grid," International Journal of Computer Theory and Engineering vol. 9, no. 3, pp. 151-155, 2017.

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