基于数据挖掘的恐怖袭击嫌疑对象判断研究
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摘要
[目的/意义]以数据驱动方式对恐怖袭击事件进行数据挖掘,有助于提高侦破效率,发现新生或者潜在的恐怖分子。[方法/过程]对全球恐怖主义数据库收录的数据进行数据挖掘,主要包括数据清洗与通过主成分分析算法完成的特征筛选,进而建立基于改进的K-means算法的聚类模型,将聚类模式中具有较多相似特征的事件进行归纳处理。[结果/结论]通过该聚类模型可以有效的将恐怖袭击事件按照嫌疑程度对嫌疑对象作出判断,为侦破恐怖袭击事件提供信息支撑。
[Purpose/significance] Data-driven data mining of terrorist attacks is helpful to improve detection efficiency and identify new or potential terrorists. [Method/process] The paper conducts data mining on data collected in Global Terrorism Database which mainly includes data cleaning and feature screening by principal component analysis algorithm,establishes a clustering model based on improved K-means algorithm,and summarizes the events with more similar characteristics in the clustering model. [Result/conclusion] It is effective to judge suspects in terrorist attacks according to the suspect degree through the clustering model mentioned above,and provide information support for terrorist attacks detection.
[Purpose/significance] Data-driven data mining of terrorist attacks is helpful to improve detection efficiency and identify new or potential terrorists. [Method/process] The paper conducts data mining on data collected in Global Terrorism Database which mainly includes data cleaning and feature screening by principal component analysis algorithm,establishes a clustering model based on improved K-means algorithm,and summarizes the events with more similar characteristics in the clustering model. [Result/conclusion] It is effective to judge suspects in terrorist attacks according to the suspect degree through the clustering model mentioned above,and provide information support for terrorist attacks detection.
引文
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[9]周涛,陆惠玲.数据挖掘中聚类算法研究进展[J].计算机工程与应用,2012,48(12):100-112.
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[13]阮越,陈汉武,陈志昊,等.量子主成分分析算法[J].计算机学报,2014,37(3),666-676.
[14]金建国.聚类方法综述[J].计算机科学,2014,41(11):288-293.
[2]侯睿.大数据时代的反恐情报收集与分析[J].山东警察学院学报,2014,26(4):94-97.
[3]陈鲁敲,张冠男.基于聚类分析法的恐怖人员分群研究[J].探索与观察,2017(5):45-46.
[4]连芷萱,兰月新,夏一雪,等.我国反恐情报分析模型研究现状与展望[J].情报杂志,2018,37(4):16-26.
[5]王沙骋.基于数据挖掘的情报主导反恐[J].情报杂志,2011,30(5):1-5.
[6]扈翔.数据挖掘方法在反恐情报分析中的应用研究[J].情报探索.2017(11):1-6.
[7]王梦雪.数据挖掘综述[J].软件导刊,2013,12(10):135-137.
[8]马延淮,穆强,田伟,等.气象数据挖掘研究[J].武汉理工大学学报,2010,32(16):110-114.
[9]周涛,陆惠玲.数据挖掘中聚类算法研究进展[J].计算机工程与应用,2012,48(12):100-112.
[10]郑斌祥,杜秀华,席裕庚.一种时序数据的离群数据挖掘新算法[J].控制与决策,2002,17(3):324-327.
[11]史尤昭.数据挖掘技术研究与应用[J].软件,2015,36(11):38-42.
[12]叶小琴,康倩飞.我国暴恐犯罪的特点与预防:基于GTD数据的统计分析[J].犯罪研究,2018(1):18-27.
[13]阮越,陈汉武,陈志昊,等.量子主成分分析算法[J].计算机学报,2014,37(3),666-676.
[14]金建国.聚类方法综述[J].计算机科学,2014,41(11):288-293.