Name
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1504/IJCSM.2020.108790 | INTERNATIONAL JOURNAL OF COMPUTING SCIENCE AND MATHEMATICS |
Keywords | DocType | Volume |
density peaks, clustering, local density, cutoff distance, cosine kernel function, density-distance | Journal | 12 |
Issue | ISSN | Citations |
1 | 1752-5055 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiayuan Wang | 1 | 1 | 1.35 |
| Li Lv | 2 | 12 | 2.25 |
| Runxiu Wu | 3 | 0 | 0.68 |
| Tanghuai Fan | 4 | 13 | 9.73 |
| Ivan Lee | 5 | 0 | 0.34 |