Authors: Chen Jungan; Chen Jinyin; Yang Dongyong
Addresses: College of Information Engineering, Zhejiang University of Technology, Hangzhou – 310023, China; College of Electronic Information, Zhejiang Wanli University, Ningbo – 315100, China ' College of Information Engineering, Zhejiang University of Technology, Hangzhou – 310023, China ' College of Information Engineering, Zhejiang University of Technology, Hangzhou – 310023, China
Abstract: For classical clustering algorithms, it is difficult to find clusters that have non-spherical shapes or varied size and density. In view of this, many methods have been proposed in recent years to overcome this problem, such as introducing more representative points per cluster, considering both interconnectivity and closeness, and adopting the density-based method. However, the density defined in DBSCAN is decided by minPts and Eps, and it is not the best solution to describe the data distribution of one cluster. In this paper, a deviation factor model is proposed to describe the data distribution and a novel clustering algorithm based on artificial immune system is presented. The experimental results show that the proposed algorithm is more effective than DBSCAN, k-means, etc.
Keywords: clustering algorithm; DBSCAN; artificial immune system; AIS; deviation factor.
International Journal of Computational Science and Engineering, 2020 Vol.21 No.2, pp.173 - 187
Received: 18 Mar 2017
Accepted: 04 Apr 2018
Published online: 11 Mar 2020 *