A GMDA clustering algorithm based on evidential reasoning architecture

作     者：Haibin WANG Xin GUAN Xiao YI Shuangming LI Guidong SUN Haibin WANG;Xin GUAN;Xiao YI;Shuangming LI;Guidong SUN

作者机构：Naval Aviation UniversityYantai 264001China Unit 92941 of PLAHuludao 125001China Institute of Systems EngineeringBeijing 100082China 

出 版 物：《Chinese Journal of Aeronautics》 (中国航空学报（英文版）)

年 卷 期：2024年第37卷第1期

页      面：300-311页

核心收录：

学科分类：11[军事学] 0714[理学-统计学（可授理学、经济学学位）] 0701[理学-数学] 1109[军事学-军事装备学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：co-supported by the Youth Foundation of National Science Foundation of China(No.62001503) the Excellent Youth Scholar of the National Defense Science and Technology Foundation of China(No.2017-JCJQ-ZQ-003) the Special Fund for Taishan Scholar Project,China(No.ts201712072) 

主　　题：Evidential clustering Credal partition Evidential reasoning Mixed decomposition Gaussian mixture model 

摘      要：The traditional clustering algorithm is difficult to deal with the identification and division of uncertain objects distributed in the overlapping region,and aimed at solving this problem,the Evidential Clustering based on General Mixture Decomposition Algorithm(GMDA-EC)is ***,the belief classification of target cluster is carried out,and the sample category of target distribution overlapping region is ***,on the basis of General Mixture Decomposition Algorithm(GMDA)clustering,the fusion model of evidence credibility and evidence relative entropy is constructed to generate the basic probability assignment of the target and achieve the belief division of the ***,the performance of the algorithm is verified by the synthetic dataset and the measured *** experimental results show that the algorithm can reflect the uncertainty of target clustering results more comprehensively than the traditional probabilistic partition clustering algorithm.