Cosine kernel based density peaks clustering algorithm

Autor:	Li Lv, Tanghuai Fan, Runxiu Wu, Ivan Lee, Jiayuan Wang
Přispěvatelé:	Wang, Jiayuan, Lv, Li, Wu, Runxiu, Fan, Tanghuai, Lee, Ivan
Rok vydání:	2020
Předmět:	Applied Mathematics Computation Boundary (topology) Theoretical Computer Science Computational Mathematics Computational Theory and Mathematics Position (vector) Kernel (statistics) Trigonometric functions Cutoff Cluster analysis Algorithm Computer Science::Information Theory Mathematics
Zdroj:	International Journal of Computing Science and Mathematics. 12:1
ISSN:	1752-5063 1752-5055
Popis:	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. Refereed/Peer-reviewed
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::94d16e27d3ca7a794c19fd8ca9f3b669 https://doi.org/10.1504/ijcsm.2020.10030824 Zobrazit plný text záznamu