聚类分析
相似性(几何)
星团(航天器)
k-最近邻算法
计算机科学
算法
数据挖掘
数学
模式识别(心理学)
人工智能
图像(数学)
程序设计语言
作者
Tanghuai Fan,Zhanfeng Yao,Longzhe Han,Baohong Liu,LV Li
摘要
The density peaks clustering (DPC) algorithm is a density-based clustering algorithm. Its density peak depends on the density-distance model to determine it. The definition of local density for samples used in DPC algorithm only considers distance between samples, while the environments of samples are neglected. This leads to the result that DPC algorithm performs poorly on complex data sets with large difference in density, flow pattern or cross-winding. In the meantime, the fault tolerance of allocation strategy for samples is relatively poor. Based on the findings, this article proposes a density peaks clustering based on k-nearest neighbors sharing (DPC-KNNS) algorithm, which uses the similarity between shared neighbors and natural neighbors to define the local density of samples and the allocation. Comparison between theoretical analysis and experiments on various synthetic and real data reveal that the algorithm proposed in this article can discover the cluster center of complex data sets with large difference in density, flow pattern or cross-winding. It can also provide effective clustering.
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