北京邮电大学学报

  • EI核心期刊

北京邮电大学学报 ›› 2025, Vol. 48 ›› Issue (1): 21-25.

• 论文 • 上一篇    下一篇

基于相对参量的自适应密度峰值聚类算法

邵壮,付卫红   

  1. 西安电子科技大学
  • 收稿日期:2023-11-01 修回日期:2023-12-26 出版日期:2025-02-26 发布日期:2025-02-25
  • 通讯作者: 付卫红 E-mail:whfu@mail.xidian.edu.cn

Adaptive Density Peak Clustering Algorithm Based on Comparative Parameters

SHAO Zhuang, FU Weihong   

  • Received:2023-11-01 Revised:2023-12-26 Online:2025-02-26 Published:2025-02-25
  • Contact: FU Wei-Hong E-mail:whfu@mail.xidian.edu.cn

摘要: 针对密度峰值聚类算法的缺陷,提出一种基于相对参量的自适应密度峰值聚类算法。所提算法通过引入新参量相对局部密度来判断簇中心,削弱数据集中不同簇的密度不同对聚类效果的影响;通过使用新参量相对连通距离来衡量簇间的相似性,消除数据集中不同簇的尺寸大小不同对聚类效果的影响,增强算法在不同数据集上的适用性;通过构造相对局部密度信息熵函数,根据数据集的特点自适应确定相关参数,提高算法智能性;采用新的点分配策略,避免链式反应。实验结果表明,相较于标准密度峰值聚类算法及其改进算法,所提算法的聚类性能有较大提升。

关键词: 聚类, 密度峰值, 自适应密度峰值聚类, 局部密度, 连通距离

Abstract:  In response to the shortcomings of the density peak clustering algorithm, a novel algorithm named adaptive density peak clustering based on comparative parameters is proposed. In the proposed algorithm, a new metric named relative local density was used to assess the similarity between clusters, which greatly improved its applicability to datasets. Another variable called relative connectivity distance was applied for measuring the similarity between clusters, which effectively eliminates the influence of different sizes of clusters in the dataset. The applicability of the algorithm on different datasets was enhanced. By constructing a comentropy function, the parameters could be adaptively determined according to the characteristics of the datasets, which improved the intelligence of the algorithm. A new point allocation strategy was proposed to avoid the effects of the ‘chain reaction’. Experimental results show that the proposed algorithm significantly improves clustering performance compared to the standard and improved density peak clustering algorithm.

Key words: clustering, density peak, adaptive density peak clustering, local density, connectivity distance

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