.png){kind=icon}
返回
A novel density deviation multi-peaks automatic clustering algorithm
.png){kind=icon}
OA
AI
DOI：10.1007/s40747-022-00798-3.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The density peaks clustering (DPC) algorithm is a classical and widely used clustering method. However, the DPC algorithm requires manual selection of cluster centers, a single way of density calculation, and cannot effectively handle low-density points. To address the above issues, we propose a novel density deviation multi-peaks automatic clustering method (AmDPC) in this paper. Firstly, we propose a new local-density and use the deviation to measure the relationship between data points and the cut-off distance (d(c)). Secondly, we divide the density deviation into multiple density levels equally and extract the points with higher distances in each density level. Finally, for the multi-peak points with higher distances at low-density levels, we merge them according to the size difference of the density deviation. We finally achieve the overall automatic clustering by processing the low-density points. To verify the performance of the method, we test the synthetic dataset, the real-world dataset, and the Olivetti Face dataset, respectively. The simulation experimental results indicate that the AmDPC method can handle low-density points more effectively and has certain effectiveness and robustness.
Keyword:
Automatic clustering
Density peaks clustering
Density deviation
Low-density points
期刊
IF:
4.6
论文数:
2.1K
被引数:
6.6K
机构
引用论文
.png){kind=icon}
Study on density peaks clustering based on k-nearest neighbors and principal component analysis基于k近邻和主成分分析的密度峰聚类研究
PREAI
分享收藏
查看更多内容