光学精密工程2024，Vol.32Issue(7)：1045-1058,14.DOI:10.37188/OPE.20243207.1045
基于自适应近邻信息的模糊C均值聚类算法
Fuzzy C-means clustering algorithm based on adaptive neighbors information
摘要
Abstract
Traditional FCM algorithms cluster based on raw data,risking distortion from noise,outliers,or other disruptions,which can degrade clustering outcomes.To bolster FCM's resilience,this study intro-duces a fuzzy C-means clustering algorithm that leverages adaptive neighbor information.This concept hinges on the similarity between data points,treating each point as a potential neighbor to others,albeit with varying degrees of similarity.By integrating the neighbor information of sample points,labeled GX,and that of cluster centers,labeled GV,into the standard FCM framework,the algorithm gains additional insights into data structure.This aids in steering the clustering process and enhances the algorithm's robust-ness.Three iterative methods are presented to implement this enhanced clustering model.When com-pared to leading clustering techniques,our approach demonstrates over a 10%improvement in cluster-ing efficacy on select benchmark datasets.It undergoes thorough evaluation across different dimen-sions,including parameter sensitivity,convergence rate,and through ablation studies,confirming its practicality and efficiency.关键词
模糊C均值聚类/自适应近邻/算法鲁棒性/迭代算法Key words
fuzzy C-means clustering/adaptive neighbors/algorithm robustness/iterative algorithm分类
计算机与自动化引用本文复制引用
高云龙,李建鹏,郑兴莘,邵桂芳,祝青园,曹超..基于自适应近邻信息的模糊C均值聚类算法[J].光学精密工程,2024,32(7):1045-1058,14.基金项目
国家自然科学基金资助项目(No.42076058,No.52075461) （No.42076058,No.52075461）
福建省自然科学基金资助项目(No.2020J01713,No.2022J01061) （No.2020J01713,No.2022J01061）
