基于聚类的恐袭事件嫌疑人与可疑据点预测

针对未知恐袭事件的相关数据,提出一种基于聚类优化的挖掘反恐信息方法,为反恐斗争提供重要情报;首先应用主成分分析对未知恐袭事件提取主要特征,采用"肘部法"选定聚类簇数确定k值,基于K-Means聚类算法对未知恐袭事件进行归类;然后通过非线性规划和聚类优化算法,将恐怖组织据点预测问题转化为无约束最优化问题,进而对恐怖主义组织据点位置进行准确估测,得到了典型事件与嫌疑人的相似度匹配,并用仿真实验推断了ISIL组织近几年在伊拉克进行恐怖袭击的据点位置;结果表明该方法对提前预警恐怖袭击有着一定的意义与价值。

Cluster-based Terrorist Attack Suspects and Suspicious Base Prediction

Aiming at the related data of unknown terrorist attacks, this paper proposes a method of mining anti terrorism information based on clustering optimization, which provides important information for the fight against terrorism. Firstly, principal component analysis is used to extract the main features of unknown attack events. The "Elbow Method" is used to select the cluster cluster number to determine the unknown attack events based on K Means clustering algorithm. Then through nonlinear programming and the clustering optimization algorithm ,the terrorist organization''s base prediction problem is transformed into an unconstrained optimization problem, and then the location of the terrorist organization is accurately estimated. The similarity between the typical event and the suspect is obtained,the simulation experiment is used to infer the position of the ISIL organization in the terrorist attack in Iraq in recent years. The results show that the method has certain significance and value for early warning of terrorist attacks.