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Mathematical Problems in Engineering
Volume 2018, Article ID 3742048, 16 pages
Research Article

A -Deviation Density Based Clustering Algorithm

1College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
2College of Electronic Information, Zhejiang Wanli University, Ningbo 315100, China

Correspondence should be addressed to Yang Dongyong; nc.ude.tujz@ydgnay

Received 2 October 2017; Revised 29 December 2017; Accepted 17 January 2018; Published 26 February 2018

Academic Editor: Erik Cuevas

Copyright © 2018 Chen Jungan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Due to the adoption of global parameters, DBSCAN fails to identify clusters with different and varied densities. To solve the problem, this paper extends DBSCAN by exploiting a new density definition and proposes a novel algorithm called -deviation density based DBSCAN (kDDBSCAN). Various datasets containing clusters with arbitrary shapes and different or varied densities are used to demonstrate the performance and investigate the feasibility and practicality of kDDBSCAN. The results show that kDDBSCAN performs better than DBSCAN.