基于密度聚类的三支K-Means聚类算法  

Three-Way K-Means Algorithm Based on Density Clustering

作　　者：李志聪[1] 晏啸昊 

机构地区：[1]哈尔滨师范大学计算机科学与工程学院,黑龙江 哈尔滨

出　　处：《计算机科学与应用》2025年第1期246-255,共10页Computer Science and Application

摘　　要：本文提出了一种基于密度聚类的三支K-Means算法。针对传统的K-Means算法在选取初始聚类中心时往往依赖于随机选择和无法处理不确定性数据对象的问题,本文采用基于密度聚类算法优化初始聚类中心的选择,并优化了截断距离的选取,最后使用三支决策的方法对聚类结果进行处理。实验结果表明,与传统的K-Means算法相比,改进的K-Means算法在聚类中表现出更高的聚类精度和稳定性。This paper proposes a three-branch K-Means algorithm based on density clustering. In view of the problem that the traditional K-Means algorithm often relies on random selection and cannot handle uncertain data objects when selecting initial clustering centers, this paper uses a density-based clustering algorithm to optimize the selection of initial clustering centers, and optimizes the selection of truncation distance. Finally, a three-branch decision method is used to process the clustering results. The experimental results show that the improved K-Means algorithm exhibits higher clustering accuracy and stability in clustering compared to the traditional K-Means algorithm.

关 键 词：K-MEANS算法 密度聚类 三支决策 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]