自适应稀疏感知密度峰值聚类算法  

Adaptive Sparse-aware Density Peaks Clustering Algorithm

作　　者：李欣娅 何星星[1] 任芮彬 LI Xin-Ya;HE Xing-Xing;REN Rui-Bin(School of Mathematics,Southwest Jiaotong University,Chengdu 611756,China)

机构地区：[1]西南交通大学数学学院,成都611756

出　　处：《计算机系统应用》2025年第2期195-205,共11页Computer Systems & Applications

基　　金：中央高校基本科研业务费专项资金(2682024ZTPY041);四川省科技计划(2023YFH0066);成都市科技项目(2023-RK00-00080-ZF)。

摘　　要：密度峰值聚类(density peaks clustering,DPC)算法通过考虑局部密度和相对距离来识别簇中心以实现聚类.然而,该算法在处理密度分布不均匀和类簇大小不平衡的数据时容易忽视低密度区域的类簇中心,需要人为设定类簇数量,并且其分配策略中一个数据点分配错误会导致后续点的错误分配.为了解决上述问题,本文提出一种自适应稀疏感知密度峰值聚类算法.首先,引入模糊点概念以降低对子簇合并过程的影响;其次,利用减法聚类方法识别低密度区域的中心;然后,根据新的局部密度和反向最近邻数来识别噪声并更新子簇中心;最后,给出改进的全局交叠度,结合全局可分度指导子簇融合,并在这些度量下自动确定聚类结果.实验结果表明,在合成数据集和UCI数据集上,与DPC及其改进算法相比,本文提出的算法能够更好地识别稀疏簇、减少非中心分配带来的连锁反应,自动确定最优类簇数目并获得更加准确的聚类结果.The density peaks clustering(DPC)algorithm achieves clustering by identifying cluster centers based on local density and relative distance.However,it tends to overlook cluster centers in low-density regions for data with uneven density distribution and unbalanced cluster sizes.Therefore,the number of clusters needs to be set artificially.Besides,if a data point allocation occurs to be wrong in the whole strategy,it will lead to incorrect allocation of subsequent points.To address these issues,this study proposes an adaptive sparse-aware density peaks clustering algorithm.Firstly,fuzzy points are introduced to minimize their impact on the subcluster merging process.Secondly,the subtractive clustering method is used to identify the low-density regions’center.Then,noise is identified and subcluster centers are updated based on new local density and reverse nearest neighbor.Finally,a redefined global overlap metric combined with global separability guides subcluster merging while automatically determining clustering results using these metrics.Experimental results demonstrate that compared to DPC and its improved algorithms,the proposed algorithm effectively identifies sparse clusters in both synthetic and UCI datasets while reducing chain reactions caused by non-center assignments.Also,the proposed algorithm can automatically determine the optimal clustering number,ultimately yielding more accurate clustering results.

关 键 词：聚类分析 密度峰值 减法聚类方法 反向近邻 子簇融合 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]