基于SOM与K-Means的铁路事故聚类可视化分析方法OA

To more clearly identify accident groups with high causal correlation and core accident-causing factors,and provide scientific references for various departments in the railway industry to formulate safety management and control strategies,thereby contributing to the systematic improvement of railway transportation safety levels.The SOM and K-Means clustering visualization analysis methods adopted in this paper can accurately identify and aggregate accident samples with similar accident-causing characteristics,and the clustering effect is significant and its effectiveness has been verified.In addition,through the construction of a railway accident topological distribution mapping model and a thermal map of accident-causing attributes,the visual presentation of railway accident clustering results has been completed.