先验原理在涉恐情报分析中的应用研究

Application of Apriori Algorithm in the Field of Counter-terrorism Intelligence Analysis

[目的/意义]在反恐工作中,对涉恐情报信息进行快速有效的分析,实现对恐怖活动的事前预警是反恐工作的关键。[方法/过程]利用基于先验原理的关联分析方法对涉恐情报进行分析,提取关联的涉恐特征属性集。通过对数据预处理后的样本数据集进行支持度计数可以产生频繁项集,然后利用频繁项集产生满足最小置信度的涉恐属性特征关联规则。[结果/结论]该文的方法可以提高涉恐情报分析的效率,其中产生频繁项集的过程较适用于发现涉恐人员,产生关联规则的过程更适用于发现不同类型的暴恐活动线索。

[ Purpose/Significance] In modem counter-terrorism work, it is very important to analyze the terrorism intelligence information quickly and build an early warning system based on the effective analysis. [ Method/Process] This paper will propose how to analyze terrorism-related information by using association rule discovery based on apriori algorithm. Frequent itemsets are generated by counting the support of sample data set after the preprocessing. All frequent itemsets except the 1 -itemset deduce the association rules of terror characteristic that satisfy the minimum confidence. [ Result/Conclusion] The method will improve the efficiency of intelligence analysis and the mechanism of terror threat warning. The process of generating frequent itemsets fits identifying potential terrorists, while the possible clues of terrorist attacks could be deduced from association rules.