一种自适应的密度峰值聚类算法

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.

An Adaptive Density Peak Clustering Algorithm

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 parameter μ_i 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.