当前位置: X-MOL 学术Knowl. Based Syst. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
A robust density peaks clustering algorithm with density-sensitive similarity
Knowledge-Based Systems ( IF 8.8 ) Pub Date : 2020-05-15 , DOI: 10.1016/j.knosys.2020.106028
Xiao Xu , Shifei Ding , Lijuan Wang , Yanru Wang

Density peaks clustering (DPC) algorithm is proposed to identify the cluster centers quickly by drawing a decision-graph without any prior knowledge. Meanwhile, DPC obtains arbitrary clusters with fewer parameters and no iteration. However, DPC has some shortcomings to be addressed before it is widely applied. Firstly, DPC is not suitable for manifold datasets because these datasets have multiple density peaks in one cluster. Secondly, the cut-off distance parameter has a great influence on the algorithm, especially on small-scale datasets. Thirdly, the method of decision-graph will cause uncertain cluster centers, which leads to wrong clustering. To address these issues, we propose a robust density peaks clustering algorithm with density-sensitive similarity (RDPC-DSS) to find accurate cluster centers on the manifold datasets. With density-sensitive similarity, the influence of the parameters on the clustering results is reduced. In addition, a novel density clustering index (DCI) instead of the decision-graph is designed to automatically determine the number of cluster centers. Extensive experimental results show that RDPC-DSS outperforms DPC and other state-of-the-art algorithms on the manifold datasets.



中文翻译:

具有密度敏感相似性的鲁棒密度峰聚类算法

提出了密度峰值聚类(DPC)算法,无需任何先验知识即可通过绘制决策图快速识别聚类中心。同时,DPC获得具有较少参数且无迭代的任意聚类。但是,DPC在广泛应用之前有一些缺点需要解决。首先,DPC不适合流形数据集,因为这些数据集在一个群集中具有多个密度峰。其次,截止距离参数对算法有很大影响,特别是对小规模数据集。第三,决策图方法将导致不确定的聚类中心,从而导致错误的聚类。为了解决这些问题,我们提出了一种具有密度敏感相似度(RDPC-DSS)的鲁棒密度峰聚类算法,以在流形数据集上找到准确的聚类中心。通过密度敏感相似性,可以减少参数对聚类结果的影响。另外,设计了新颖的密度聚类指数(DCI)代替决策图来自动确定聚类中心的数量。大量的实验结果表明,在流形数据集上,RDPC-DSS优于DPC和其他最新算法。

更新日期:2020-05-15
down
wechat
bug