计算机工程2025，Vol.51Issue(4)：137-148,12.DOI:10.19678/j.issn.1000-3428.0068956
基于高斯分布的自适应密度峰值聚类算法
Adaptive Density Peak Clustering Algorithm Based on Gaussian Distribution
摘要
Abstract
The Density Peak Clustering(DPC)algorithm excels in diverse fields,is adept at identifying clusters of any shape,and is noise-resistant.However,the algorithm needs help with manual cluster center selection and underperforms on datasets with uneven densities.This paper introduces a novel Gaussian distribution-based adaptive DPC algorithm to overcome these challenges.This approach involves multiplying the local density by the relative distance θi and mapping this θi into a two-dimensional Gaussian space using Z-score standardization.Uniquely,the algorithm adaptively selects cluster centers based on the standard deviation of the Gaussian distribution and assigns data points to their nearest centers for initial clustering.This paper also introduces a suture factor model to facilitate the merging of similar sub-clusters.When the suture coefficient is greater than the threshold,merge the most similar clusters in the preliminary partition results and update the similarity matrix until the merging process is completed to obtain the final result.The experimental results on artificial and real datasets indicate that compared with DBSCAN algorithm,DPC algorithm,and ICKDC algorithm,the proposed algorithm has higher clustering accuracy and better clustering performance.关键词
密度峰值聚类算法/高斯分布/Z-score标准化/缝合因子/簇间相似度Key words
Density Peak Clustering(DPC)algorithm/Gaussian distribution/Z-score standardization/suture factor/inter-cluster similarity分类
信息技术与安全科学引用本文复制引用
李启文,王治和,杜辉,鲁德鹏..基于高斯分布的自适应密度峰值聚类算法[J].计算机工程,2025,51(4):137-148,12.基金项目
国家自然科学基金(62372353). （62372353）
