基于两阶段搜索的密度聚类算法  

Density clustering algorithm based on two-stage search

作　　者：汪勇[1] 李巧娜 艾学轶 WANG Yong;LI Qiao-na;AI Xue-yi(Evergrande School of Management,Wuhan University of Science and Technology,Wuhan 430081,China)

机构地区：[1]武汉科技大学恒大管理学院,湖北武汉430081

出　　处：《计算机工程与设计》2023年第1期188-193,共6页Computer Engineering and Design

基　　金：国家自然科学基金项目(71901167)。

摘　　要：为克服当前密度聚类算法存在的随机性、主观性和连带错误等问题,提出一种基于两阶段搜索的密度聚类算法。给出密度阈值和簇最近邻定义及计算方法。采用密度排序、簇最近邻分配和自适应搜索策略构建算法的两阶段聚类机制,设计邻域递归搜索和簇最近邻搜索两个阶段的聚类算法,实现不同密度数据点的准确聚类。8个数据集聚类实验结果表明,该密度聚类算法聚类稳定,无噪声,且自动确定类簇数,聚类精度优于比较的密度聚类算法。To overcome the problems of stochasticity,subjectivity and joint error of the current density clustering algorithms,a kind of density clustering algorithm based on two-stage search was proposed.The definitions and calculating approaches of density threshold and the nearest neighborhood in clusters were given.The two-stage clustering mechanism was built using the strategies of density sorting,distribution according to the nearest neighborhood in clusters and adaptive search,and the two-stage clustering algorithms of recursive search in neighborhood and the nearest neighbor search in clusters were designed,realizing accurate clustering of data points with different densities.Results of clustering experiments on eight datasets show that the proposed density clustering algorithm is stable,noiseless,and automatically determines the number of clusters.The clustering accuracy is better than that of the comparison density clustering algorithms.

关 键 词：聚类算法 密度聚类 算法设计 两阶段搜索 密度阈值 簇最近邻 分配策略 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]