现代信息科技2025，Vol.9Issue(6)：33-38,45,7.DOI:10.19850/j.cnki.2096-4706.2025.06.007
基于隶属度条件放宽的模糊聚类目标检测算法
Fuzzy Clustering Target Detection Algorithm Based on Membership Degree Condition Relaxation
摘要
Abstract
The research object of this paper is the Fuzzy C-Means(FCM)clustering algorithm,which aims to improve the effectiveness of FCM in dealing with complex data.Traditional FCM assumes that the sum of the membership degree of a sample to each cluster is 1,which is called sample contribution degree.However,when dealing with noise points and outliers,the effectiveness of clustering may decrease.To this end,this paper proposes a new fuzzy clustering method.By relaxing the condition that the sum of membership degree is 1,a new membership degree division method is introduced,that is,the sum of membership degree is an innovative definition of the total number of samples n.This method dynamically adjusts the membership degree according to the distance between the sample and the clustering center,and divides the membership degree into three levels to improve the effectiveness of clustering.Through mathematical derivation and experimental verification,the improved algorithm improves the accuracy by 6.41%and the recall rate by 5.81%when dealing with complex data sets.It significantly improves the performance of the algorithm.关键词
模糊C均值/隶属度/样本贡献度/目标检测Key words
FCM/membership degree/sample contribution degree/Target Detection分类
信息技术与安全科学引用本文复制引用
钱司远,沈雅婷,田瑞淼,王正东,顾宇航,汤风铃..基于隶属度条件放宽的模糊聚类目标检测算法[J].现代信息科技,2025,9(6):33-38,45,7.基金项目
江苏省高校"青蓝计划"资助 （）
2024江苏省创新训练通用资助项目(202413654018Y) （202413654018Y）
2023南京理工大学紫金学院大学生创新训练计划资助项目(ZJSRTP2023118) （ZJSRTP2023118）
