.png){kind=icon}
返回
A GMDA clustering algorithm based on evidential reasoning architecture
.png){kind=icon}
OA
AI
DOI：10.1016/j.cja.2023.09.015.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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. (c) 2023 Production and hosting by Elsevier Ltd. on behalf of Chinese Society of Aeronautics and Astronautics. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).
Keyword:
Evidential clustering
Credal partition
Evidential reasoning
Mixed decomposition
Gaussian mixture model
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
5.7
论文数:
4.7K
被引数:
1.4W
机构
暂无机构信息
引用论文
分享收藏
Collaborative feature-weighted multi-view fuzzy c-means clustering协同特征加权多视图模糊c均值聚类.png)
PATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
分享收藏分享收藏Evaluating and Comparing Soft Partitions: An Approach Based on Dempster-Shafer Theory评估和比较软分区: 一种基于dempster-shafer理论的方法OAAI
OAAI分享收藏
查看更多内容