Enhanced association rule mining for gas cylinder accident analysis

A thorough analysis of underlying factors is crucial to mitigate the risk of gas cylinder accidents. This paper introduces an improved Apriori algorithm that weights accident severity to enhance the accuracy of risk factor identification. By refining the calculation of support and confidence metrics, the algorithm enables a more quantitative analysis of these factors. Furthermore, the study delves into the association rules between different gas cylinder types, accident attributes, and risk factors, providing valuable insights into accident causation. The research outcomes demonstrate the algorithm's effectiveness in reducing redundant information and improving mining efficiency. This algorithm offers useful guidance for proactive safety measures and preventive strategies by uncovering significant associations between risk factors and accidents.