Density peaks clustering algorithm (DPC) has attracted the attention of many scholars because of its multiple advantages, including efficiently determining cluster centers, a lower number of parameters, no iterations, and no border noise. However, DPC does not provide a reliable and specific selection method of threshold (cutoff distance) and an automatic selection strategy of cluster centers. In this paper, we propose density peaks clustering by zero-pointed samples (DPC-ZPSs) of regional group borders. DPC-ZPS finds the subclusters and the cluster borders by zero-pointed samples (ZPSs). And then, subclusters are merged into individuals by comparing the density of edge samples. By iteration of the merger, the suitable dc and cluster centers are ensured. Finally, we compared state-of-the-art methods with our proposal in public datasets. Experiments show that our algorithm automatically determines cutoff distance and centers accurately.

中文翻译：

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通过区域组边界的零点样本进行密度峰聚类。

密度峰聚类算法（DPC）由于具有多重优势，包括有效地确定聚类中心，减少参数数量，无需迭代且没有边界噪声，因此吸引了许多学者的注意。但是，DPC没有提供可靠和特定的阈值（截止距离）选择方法以及群集中心的自动选择策略。在本文中，我们提出了由区域群边界的零点样本（DPC-ZPS）进行的密度峰聚类。DPC-ZPS通过零点样本（ZPS）查找子群集和群集边界。然后，通过比较边缘样本的密度，将子集群合并为个体。通过合并的迭代，确保了合适的DC和群集中心。最后，我们在公共数据集中将最新方法与我们的建议进行了比较。