摘要
[目的/意义]利用显露模式挖掘对不同类别的涉恐情报数据集进行分析,可以发现那些对反恐工作有参考价值的差异信息。[方法/过程]根据反恐工作的需求和显露模式的特点,通过修改经典方法中的数据预处理、目标事务集生成、最大边界压缩、非交集属性特征分离等步骤,使其更适用于反恐情报的快速分析。[结果/结论]在频繁项集挖掘的基础上,显露模式可以发现一些反恐情报的多组属性聚合规律用于分类,更快速的为反恐预警提供数据参考。
[Purpose/Significance]It offered valuable intelligence with remarkable difference for counter terrorism to analyze terror related data sets in different categories using emerging pattern mining.[Method/Process]According to the needs and characteristics of data mining system of counter terrorism,several steps in the classical method were modified such as data preprocessing,generation of objective transactions,compression of Largeborder and stripping attributes of non-intersection,so as to make it more adaptable to the needs of intelligence analysis.[Result/Conclusion]On the basis of mining frequent itemsets,emerging pattern could find the merging rules of multiple attributes to provide data references for early warning of counter terrorism.
作者
李勇男
Li Yongnan(People's Public Security University of China,Beijing 100038,China)
出处
《现代情报》
CSSCI
2020年第5期27-32,共6页
Journal of Modern Information
基金
中国人民公安大学基本科研业务费项目“基于显露模式挖掘的反恐情报分类对比分析”(项目编号:2020JKF305)
北京高校高精尖学科中国人民公安大学国家安全学学科建设项目。
关键词
反恐情报
数据挖掘
显露模式
边界
counter terrorism intelligence
data mining
emerging pattern
border
