多种聚类算法对核爆炸数据的分析与模式识别OA

The analysis of nuclear-explosion data underpins assessments of weapon effectiveness,strategic security,and the global nuclear-safety monitoring architecture.Accurate interpretation of these data is therefore directly linked to national-security decisions and calibration of international strategic balances.Yet the signals generated by nuclear events are extraordinarily complex,and conventional analytical techniques often fail to extract their full informational content.To overcome these limitations,a pattern-recognition framework leveraging advanced clustering algorithms was presented.After assembling a comprehensive,multi-parameter dataset of nuclear-explosion signatures,an exploratory analysis using network-relation graphs,t-SNE,and scatter-plot matrices to reveal latent structure was conducted.Then three unsupervised clustering algorithms—K-means,fuzzy C-means,and density-based spatial clustering of applications with noise with respect to their ability to discriminate among events on the basis of explosion yield,source depth,and other critical observables were evaluated.These results demonstrate that it is achieved 76.92％accuracy using density-based spatial clustering of applications with noise to identify subtle groupings that elude centroid-oriented methods.The proposed approach therefore offers a robust,data-driven foundation for nuclear-weapon research,testing protocols,strategic deployment,and security assessment,and can be used to develop related fields.