Recently the density peaks clustering algorithm (DPC) has received a lot of attention from researchers. The DPC algorithm is able to find cluster centers and complete clustering tasks quickly. It is also suitable for different kinds of clustering tasks. However, deciding the cutoff distance \({d}_{c}\) largely depends on human experience which greatly affects clustering results. In addition, the selection of cluster centers requires manual participation which affects the efficiency of the algorithm. In order to solve these problems, we propose a density peaks clustering algorithm based on K nearest neighbors with adaptive merging strategy (KNN-ADPC). A clusters merging strategy is proposed to automatically aggregate over-segmented clusters. Additionally, the K nearest neighbors are adopted to divide data points more reasonably. There is only one parameter in KNN-ADPC algorithm, and the clustering task can be conducted automatically without human involvement. The experiment results on artificial and real-world datasets prove higher accuracy of KNN-ADPC compared with DBSCAN, K-means++, DPC, and DPC-KNN.

最近，密度峰值聚类算法（DPC）受到了研究人员的广泛关注。DPC算法能够快速找到聚类中心并完成聚类任务。它也适用于不同种类的聚类任务。然而，决定截止距离\({d}_{c}\)很大程度上取决于人类经验，这极大地影响了聚类结果。另外，聚类中心的选择需要人工参与，影响算法效率。为了解决这些问题，我们提出了一种基于K个最近邻自适应合并策略的密度峰值聚类算法（KNN-ADPC）。提出了一种集群合并策略来自动聚合过度分割的集群。此外，采用K个最近邻来更合理地划分数据点。KNN-ADPC算法中只有一个参数，聚类任务可以自动进行，无需人工干预。在人工和真实世界数据集上的实验结果证明，与 DBSCAN、K-means++、DPC 和 DPC-KNN 相比，KNN-ADPC 具有更高的准确性。