The density-peak clustering (DPC) method (Rodriguez and Laio, 2014) clusters the data efficiently by fast searching density peaks. Recently, an improved DPC algorithm named 3DC method (Liang and Chen, 2016) was proposed for automatically detecting the correct structure of the clusters. However, it is difficult to select correct parameters for the DPC and 3DC methods and the local property of data set can’t be revealed due to their global density assumption in some scenarios. To overcome this drawback, the K-nearest neighbor (KNN) framework is adapted for defining the density of the DPC method. Nevertheless, such KNN-based methods can’t automatically detect the number of the clusters compared with the 3DC method. In this paper, an automatic clustering method is proposed, which needs only a discrete input parameter. Meanwhile, by utilizing the cluster stability for the Chameleon framework, the proposed method can automatically detect the correct structure of the clusters. The experimental results on the synthetic and real world data demonstrate that the proposed method has a more robust performance. Besides, the proposed method is robust to the choices of the input parameter.

密度峰值聚类 (DPC) 方法（Rodriguez 和 Laio，2014 年）通过快速搜索密度峰值有效地聚类数据。最近，提出了一种名为 3DC 方法的改进 DPC 算法（Liang and Chen，2016），用于自动检测簇的正确结构。然而，DPC 和 3DC 方法很难选择正确的参数，并且在某些情况下由于它们的全局密度假设无法揭示数据集的局部属性。为了克服这个缺点，K-最近邻 (KNN) 框架适用于定义 DPC 方法的密度。然而，与 3DC 方法相比，这种基于 KNN 的方法无法自动检测集群的数量。本文提出了一种自动聚类方法，该方法只需要一个离散的输入参数。同时，通过利用 Chameleon 框架的集群稳定性，所提出的方法可以自动检测集群的正确结构。对合成数据和真实世界数据的实验结果表明，所提出的方法具有更稳健的性能。此外，所提出的方法对输入参数的选择具有鲁棒性。