| Summary: | In order to analyze the relationship among various factors in infrared countermeasures and their relationship with missile miss distance, association rules are mined based on FP-Growth algorithm. In the process of infrared countermeasure mining, K-means clustering algorithm is used to discretize the continuous factors such as docey throwing time, missile entry angle and initial distance of missile and target. Then, Kulc and IR indexes are used to deeply screen the obtained association rules. Two screening indexes filter out 69 ineffective association rules, and the final rules are obtained. The results show that the association rule mining method is effective and feasible in the research of infrared anti-jamming evaluation, and can provide research ideas for the analysis of infrared countermeasure problems.
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