A k-Deviation Density Based Clustering Algorithm. (26th February 2018)

• Record Type: Journal Article
• Authors: Jungan, Chen; Jinyin, Chen; Dongyong, Yang; Jun, Li
• Other Names: Cuevas Erik Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2018(2018)
• Publication Date: 2018-02-26
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2018/3742048
• Languages: English
• ISSNs: 1024-123X

Abstract

Abstract : 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 calledk -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.