【目的/意義】利用數據挖掘技術在海量數據中快速、準確、有效的發現涉恐線索并及時處置是反恐工作的重要手段之一。【方法/過程】本文研究如何利用頻繁模式樹對涉恐基礎數據進行挖掘,提取涉恐特征的頻繁項集。首先通過對數據庫中的涉恐人員信息進行涉恐特征計數排序并建立頻繁模式樹,然后在樹結構中遞歸遍歷發現滿足最小支持度閾值的頻繁項集。【結果/結論】文中的方法可以快速發現大量基礎數據中的涉恐關聯屬性,有利于在系統中自動搜索重點涉恐人員,為反恐預警系統提供決策參考。通過與其他產生關聯規則的方法結合使用,還可以發現暴恐活動中不同因素的因果關系。

【Purpose/significance】It is one of the most important methods to accurately find valid terrorist activity clues in time from mass data by using data mining technologies.【Method/process】This paper proposes how to extract frequent itemsets of terror related features by using frequent pattern-growth algorithm.These features are sorted by counting the numbers to construct frequent pattern tree.The frequent itemsets are generated by traversing the tree structure recursively.【Result/conclusion】This method could quickly find the associated terror related features.These relationships are conducive to search the key person automatically from the database in the system and provide decision references for terror threat warning.With the help of association rule mining,this method is also useful to find the potential terrorist activities.