基于非参数核密度估计的密度峰值聚类算法*

针对密度峰值聚类算法CFSFDP(Clustering by fast search and find of density peaks)计算密度时人为判断截断距离和人工截取簇类中心的缺陷，提出了一种基于非参数核密度估计的密度峰值的聚类算法。首先，应用非参数核密度估计方法计算数据点的局部密度；其次，根据排序图采用簇中心点自动选择策略确定潜在簇类中心点，将其余数据点归并到相应的簇类中心；最后，依据簇类间的合并准则，对邻近相似子簇进行合并，并根据边界密度识别噪声点，得到聚类结果。在人工测试数据集和UCI真实数据集上的实验表明，新算法较之原CFSFDP算法，不仅有效避免了人为判断截断距离和截取簇类中心的主观因素，而且可以取得更高的准确度。

Density peak clustering algorithm based on non-parametric kernel density estimation

In view of the problem that the density peak clustering algorithm CFSFDP (Clustering by fast search and find of density peaks) cannot determine the cut off distance and the interception of the cluster center, this paper put forward an improved algorithm based on non-parametric kernel density estimation. Firstly, this paper used the non-parametric kernel density estimation method to calculate the local density of the data points. Secondly, according to the sort map, it used the cluster center point automatic selection strategy to determine the cluster center point, then merged the rest of the data points to the corresponding cluster center. Finally, this paper merged the neighboring similar clusters based on the merging criterion between the clusters, and identified the noise points according to the boundary density. Experiments on the manual test data set and the UCI real data set show that the new algorithm can not only determine the cluster center automatically, but also get higher accuracy than the original CFSFDP algorithm.