K-DBSCAN: an efficient density-based clustering algorithm supports parallel computing
by Chao Deng; Jinwei Song; Saihua Cai; Ruizhi Sun; Yinxue Shi; Shangbo Hao
International Journal of Simulation and Process Modelling (IJSPM), Vol. 13, No. 5, 2018

Abstract: DBSCAN is the most representative density-based clustering algorithm and has been widely used in many fields. However, the running time of DBSCAN is unacceptable in many actual applications. To improve its performance, this paper presents a new 2D density-based clustering algorithm, K-DBSCAN, which successfully reduces the computational complexity of the clustering process by a simplified k-mean partitioning process and a reachable partition index, and enables parallel computing by a divide-and-conquer method. The experiments show that K-DBSCAN achieves remarkable accuracy, efficiency and applicability compared with conventional DBSCAN algorithms especially in large-scale spatial density-based clustering. The time complexity of K-DBSCAN is O(N²/KC), where K is the number of data partitions, and C is the number of physical computing cores.

Online publication date: Fri, 14-Sep-2018

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Simulation and Process Modelling (IJSPM):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com