DBSCAN-PSM: an improvement method of DBSCAN algorithm on Spark
by Guangsheng Chen; Yiqun Cheng; Weipeng Jing
International Journal of High Performance Computing and Networking (IJHPCN), Vol. 13, No. 4, 2019

Abstract: DBSCAN is a density-based data clustering algorithm; in image processing, data mining, machine learning and other fields are widely used. With the increasing of the size of clusters, the parallel DBSCAN algorithm is widely used. However, we consider current partitioning method of DBSCAN is too simple and steps of GETNEIGHBORS query repeatedly access the dataset on Spark. So we proposed DBSCAN-PSM which applies new data partitioning and merging method. In the first stage of our method, we import the KD-tree, combine the partitioning and GETNEIGHBORS query, reduce the number of access to the dataset and decrease the influence of I/O in the algorithm. In the second stage of our method, we use the feature of points in merging so as to avoid the time costing of the global label. Experimental results showed that our new method can improve the parallel efficiency and the clustering algorithm performance.

Online publication date: Wed, 24-Apr-2019

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 High Performance Computing and Networking (IJHPCN):
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