一种自适应的密度峰值聚类算法

An Adaptive Density Peak Clustering Algorithm

针对密度峰值聚类(density peak clustering,DPC)算法不能根据数据集自适应选取聚类中心和截断距离d_(c),从而不能自适应聚类的问题,提出了一种自适应的密度峰值聚类(adaptive density peak clustering,ADPC)算法.首先,提出了一个综合考虑局部密度ρ_(i)和相对距离δ_(i)的参数μ_(i),根据μ_(i)的排列顺序及下降趋势trend自动确定聚类中心.然后,基于基尼系数G对截断距离d_(c)做了自适应选择.最后,对ADPC算法做出了实验验证,并与DPC算法和K-means算法进行了对比.实验结果表明,ADPC算法具有较高的ARI,NMI和AC值,具有较好的聚类效果.

The density peak clustering algorithm cannot adaptively cluster because it cannot adaptively select the clustering center and cutoff distance d_(c) according to data set,so that an adaptive density peak clustering(ADPC)algorithm was proposed.Firstly,a parameterμ;that comprehensively considers the local density ρ_(i) and cutoff distance δ_(i) was proposed,and the cluster center was automatically determined according to the sorting and downtrend of μ_(i).Then,an adaptive selection of d_(c) was made based on the concept of Gini coefficient.Finally,the ADPC algorithm was verified and compared with the DPC and K-means algorithm.The experimental results show that the ADPC algorithm has higher ARI,NMI and AC values,and has a better clustering effect.