Title: Cosine kernel based density peaks clustering algorithm

Authors: Jiayuan Wang; Li Lv; Runxiu Wu; Tanghuai Fan; Ivan Lee

Addresses: School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China ' School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China ' School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China ' School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China ' School of Information Technology and Mathematical Sciences, University of South Australia, Adelaide 5000, Australia

Abstract: Density peaks clustering (DPC) determines the density peaks according to density-distance, and local density computation significantly impacts the clustering performance of the DPC algorithm. Following this lead, a revised DPC algorithm based on cosine kernel is proposed and examined in this paper. The cosine kernel function uses local information of datasets to define the local density, which not only finds the position difference of different samples within the cutoff distance, but also balances the influence of centre points and boundary points of clusters on local density of samples. Theoretical analysis and experimental verification are included to demonstrate the proposed algorithm's improvement in clustering performance and computational time over the DPC algorithm.

Keywords: density peaks; clustering; local density; cutoff distance; cosine kernel function; density-distance.

DOI: 10.1504/IJCSM.2020.108790

International Journal of Computing Science and Mathematics, 2020 Vol.12 No.1, pp.1 - 20

Received: 31 Oct 2019
Accepted: 25 Nov 2019

Published online: 27 Jul 2020 *

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