Prediction of past unsolved terrorist attacks

Fatih Özgül, Z. Erdem, C. Bowerman
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引用次数: 13

Abstract

In this study, a novel model is proposed to predict perpetuators of some terrorist events which are remain unsolved. The CPM learns from similarities between terrorist attacks and their crime attributes then puts them in appropriate clusters. Solved and unsolved attacks are gathered in the same - all linked to each other - “umbrella” clusters; then CPM classifies all related terrorist events which are expected to belong to one single terrorist group. The developed model is applied to a real crime dataset, which includes solved and unsolved terrorist attacks and crimes in Turkey between 1970 and 2005. CPM predictions produced significant precision value for big terrorist groups and reasonable recall values for small terrorist groups.
预测过去未解决的恐怖袭击
在本研究中,提出了一个新的模型来预测一些尚未解决的恐怖事件的延续者。CPM从恐怖袭击及其犯罪属性之间的相似性中学习,然后将它们放在适当的集群中。已解决和未解决的攻击都聚集在同一个“保护伞”集群中——所有攻击都彼此相连;然后CPM将所有相关的恐怖事件分类,这些事件可能属于一个恐怖组织。开发的模型应用于真实的犯罪数据集,其中包括1970年至2005年间土耳其已解决和未解决的恐怖袭击和犯罪。CPM预测对大型恐怖组织具有显著的精度值,对小型恐怖组织具有合理的召回值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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