A GMDA clustering algorithm based on evidential reasoning architecture

Abstract

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 proposed. First, the belief classification of target cluster is carried out, and the sample category of target distribution overlapping region is extended. Then, 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 target. Finally, the performance of the algorithm is verified by the synthetic dataset and the measured dataset. The experimental results show that the algorithm can reflect the uncertainty of target clustering results more comprehensively than the traditional probabilistic partition clustering algorithm.