引用本文:赵嘉,陈蔚昌,肖人彬,潘正祥,崔志华,王晖.面向流形数据的共享近邻和二阶K近邻密度峰值聚类算法[J].控制理论与应用,2026,43(2):386~394.[点击复制]
ZHAO Jia,CHEN Wei-chang,XIAO Ren-bin,PAN Zheng-xiang,CUI Zhi-hua,WANG Hui.Density peaks clustering algorithm based on shared nearest neighbor and second-order K nearest neighbor for manifold data[J].Control Theory & Applications,2026,43(2):386~394.[点击复制]
面向流形数据的共享近邻和二阶K近邻密度峰值聚类算法
Density peaks clustering algorithm based on shared nearest neighbor and second-order K nearest neighbor for manifold data
摘要点击 130  全文点击 21  投稿时间：2023-08-22  修订日期：2025-02-25
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DOI编号  10.7641/CTA.2024.30570
  2026,43(2):386-394
中文关键词  密度峰值聚类  逆近邻  共享近邻  二阶K近邻  流形数据
英文关键词  density peaks clustering  reverse nearest neighbor  shared nearest neighbor  second-order K nearest neighbor  manifold data
基金项目  国家自然科学基金项目(62466037, 62166027)资助.
作者单位E-mail
赵嘉* 南昌工程学院 信息工程学院 zhaojia925@163.com 
陈蔚昌 南昌工程学院 信息工程学院  
肖人彬 华中科技大学 人工智能与自动化学院  
潘正祥 山东科技大学 计算机科学与工程学院  
崔志华 太原科技大学 计算机科学与技术学院  
王晖 南昌工程学院 信息工程学院  
中文摘要
      密度峰值聚类算法能够快速高效处理数据集且无需迭代. 但该算法在处理流形数据时, 易错选类簇中心和 错误分配样本. 因此, 本文提出面向流形数据的共享近邻和二阶K近邻密度峰值聚类(DPC–SKNN)算法. 首先, 该算 法引入逆近邻和共享近邻重新定义局部密度, 充分考虑样本的局部信息和全局信息, 使算法易找到正确的流形类簇 中心; 其次, 将样本的关联关系分为K近邻点、二阶K近邻点和非近邻点3种情况, 设计K近邻的分配策略, 增强同一 类簇样本的相似性, 提高样本分配的准确率. 将本文算法与8种算法在流形和UCI数据集进行对比, 实验结果表明, DPC-SKNN算法在上述数据集上均获得了不错的聚类结果.
英文摘要
      The density peaks clustering algorithm can deal with datasets quickly and efficiently without iteration. However, it can sometimes wrongly select cluster centers and misallocate samples when processing manifold data. Therefore, this paper proposes the density peaks clustering algorithm based on shared nearest neighbor and second-order K nearest neighbor for manifold data (DPC-SKNN) algorithm. Firstly, the algorithm introduces reverse nearest neighbors and shares nearest neighbors to redefine local density, fully considering both local and global information of samples, making the algorithm easier to identify correct cluster centers. Secondly, the association relationship of the samples is divided into three types: K nearest neighbors, second-order K nearest neighbors, and non-nearest neighbors, and design allocation strategies for K-nearest neighbors to enhance similarity among samples within the same cluster, thereby improving sample allocation accuracy. DPC-SKNN is compared with eight algorithms on manifold and UCI datasets, and the experimental results show that the DPC-SKNN algorithm obtains good clustering results on all the above datasets.