Abstract

The frequent occurrence of emergencies in recent years has seriously affected the stability and development of society and economy. Effectively responding to emergencies has become a crucial research topic. A large number of emergency cases contain potential rules and valuable response experience in the process of occurrence, development and response, and the association rule mining can discover the fine-grained and valuable "if scenario then response" association rules in the cases. The knowledge of emergency response rules obtained from mining can assist the generation of response programs under actual emergency scenarios and provide reference basis for emergency decision-making. The existing methods only consider the support and confidence indicators but ignore the lift and comprehensibility of the rules and the lack of domain knowledge guidance in the mining process. Aiming at the problem, this paper proposes a response rule mining method based on knowledge guidance and improved genetic algorithm, which effectively reduces the number of low-quality rules and attributes of the mined rules and improves the quality and efficiency of association rule mining.