基于数据挖掘的全球恐怖主义数据库数据分析 |
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引用本文:李永群 1 ,应万明 2 ,袁 飞 3 ,韩玉春 3.基于数据挖掘的全球恐怖主义数据库数据分析[J].经济数学,2019,(2):91-94 |
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中文摘要:运用数据挖掘的方法,对全球恐怖主义数据库(以下简称GTD)进行了量化分析.建立了基于KNN邻近算法的恐怖袭击事件量化分级模型和基于K-means聚类算法的恐怖袭击事件分类模型.此外,对近三年来恐怖袭击事件发生的主要原因、时空特性、蔓延特性以及级别分布规律进行了分析.最后,基于建立的模型和分析结论,对未来全球和某些重点地区的反恐态势进行了预测分析,给出了具有针对性的建议. |
中文关键词:应用统计数学 恐怖袭击 数据挖掘 KNN K-means |
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Data Analysis of GTD Based on Data Mining |
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Abstract:Use data mining methods to investigate the Global Terrorism Database (GTD). Quantitative grading model based on KNN algorithm and classification model based on K-means clustering algorithm about terrorist attacks are established respectively. Furthermore, the main reasons, time and space characteristics, spread features and level distributions of terrorist attacks in the past three years are studied and analyzed. According to this paper’s models and conclusions, the global and some key regions anti-terrorism situations in the future are researched and judged, and recommendations for the fight against terrorism are given. |
keywords:applied statistical mathematics terrorist attack data mining KNN K-means |
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