A k -Deviation Density Based Clustering Algorithm

Year: 2018

Type: article

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 called k -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.

Source: Mathematical Problems in Engineering

DOI: 10.1155/2018/3742048

Authors: Chen Jungan, Chen Jinyin, Yang Dongyong, Li Jun

Institutions: Zhejiang Wanli University, Zhejiang University of Technology

Language: English

Topic: Advanced Clustering Algorithms Research

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $2,550

Funders: National Natural Science Foundation of China, Natural Science Foundation of Ningbo, Natural Science Foundation of Zhejiang Province

OpenAlex ID: W2794211190