基于新型阴影集的模糊C均值聚类算法  

Fuzzy C-means clustering algorithm based on new shadowed sets

作　　者：国栋凯 张钦然 李小南 易黄建[1] GUO Dongkai;ZHANG Qinran;LI Xiaonan;YI Huangjian(School of Information Science and Technology,Northwest University,Xi'an 710127,Shaanxi,China;School of Mathematics and Statistics,Xidian University,Xi'an 710126,Shaanxi,China)

机构地区：[1]西北大学信息科学与技术学院,陕西西安710127 [2]西安电子科技大学数学与统计学院,陕西西安710126

出　　处：《山东大学学报(理学版)》2025年第1期74-82,共9页Journal of Shandong University(Natural Science)

基　　金：国家自然科学基金资助项目(61906154);陕西省教育厅青年创新团队资助项目(21JP123)。

摘　　要：提出一种基于五区域阴影集的模糊C均值(fuzzy C-means,FCM)算法,利用FCM算法得到对象簇的隶属度,引入五区域阴影集,将对象划分为核心区域、次核心区域、阴影区域、次边缘区域和边缘区域,分析次核心区域得到阈值ω,通过核心区域和次核心区域中隶属度μ≥ω的对象簇得到聚类结果,在8个公开数据集中进行实验。本文提出的算法相比于其余3种算法在7个数据集上取得了最佳的聚类结果。A fuzzy C-means(fuzzy C-means,FCM)clustering algorithm based on five-region shadowed sets is proposed in this paper.The membership degree of the object to the cluster is obtained by the FCM algorithm.The object is divided into core region,semi-core region,shadow region,semi-negative region and negative region according to the membership degree by introducing the five-region shadowed sets.Then,a threshold value ω is obtained by analyzing the semi-core region.The objects whose membership degree μ≥ω in the core region and semi-core region are classified into this cluster to get the final clustering result.Experiments are carried out on 8 public data sets with other 3 clustering algorithms,compared with the other 3 algorithms,the algorithm proposed in this paper achieves the best clustering results on 7 data sets.The experimental results show that the proposed algorithm in this paper is superior to 3 other algorithms.

关 键 词：三支决策 模糊聚类 三支聚类 五区域阴影集 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]