改进的聚类算法在恐怖袭击事件中的应用

何庆祥; 张巍

doi:10.12052/gdutxb.190052

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改进的聚类算法在恐怖袭击事件中的应用

综合研究 | 更新时间：2026-05-27

- 改进的聚类算法在恐怖袭击事件中的应用
- Application of Improved Clustering Algorithm in Terrorist Attacks
- 广东工业大学学报 2019年36卷第4期页码：24-30
- 作者机构：
  
  广东工业大学计算机学院,广东,广州,510006
- 作者简介：
  
  何庆祥(1994–)，男，硕士研究生，主要研究方向为数据挖掘、大数据
- 基金信息：
  
  国家自然科学基金资助项目(61402118，61673123，61603100，61702110，61772141);教育部高教司项目(教高司函[2017]47号，教高司函[2018]4号);广东省科技计划项目(2015B090901016，2016B010108007);广东省教育厅项目(粤教高函[2018]179号，粤教高函[2018]1号，粤教高函[2015]133号，粤教高函[2014] 97号);广州市科技计划项目(201604020145，2016201604030034，201508010067，201604046017，201802030011，201802010042，201802010026)
- DOI：10.12052/gdutxb.190052
  中图分类号： TP391
- 网络首发：2019-06-18，
  
  纸质出版：2019
- 稿件说明：
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何庆祥, 张巍. 改进的聚类算法在恐怖袭击事件中的应用[J]. 广东工业大学学报, 2019, 36(4): 24-30.

He Qing-xiang, Zhang Wei. Application of Improved Clustering Algorithm in Terrorist Attacks[J]. 2019, 36(4): 24-30.
何庆祥, 张巍. 改进的聚类算法在恐怖袭击事件中的应用[J]. 广东工业大学学报, 2019, 36(4): 24-30. DOI： 10.12052/gdutxb.190052.

He Qing-xiang, Zhang Wei. Application of Improved Clustering Algorithm in Terrorist Attacks[J]. 2019, 36(4): 24-30. DOI： 10.12052/gdutxb.190052.

摘要

恐怖袭击严重影响国际社会的稳定和人们生命财产安全

其形式、手段的多样化给反恐分析带来巨大挑战. 为了把相似的恐怖袭击事件进行分组归类

并提高反恐分析员侦破案件的效率

本文基于全球恐怖主义数据库

提出了一种深度自编码表征(Deep Auto-Encoder Representation)的改进聚类算法

引入深度自编码器

将稀疏和嘈杂的原始数据映射为类内紧凑平滑的数据

提升聚类效果. 实验结果表明

相比于传统的K-means聚类算法

改进后的算法可以提高聚类效果. 本方法有利于反恐分析员将相似案件并案分析处理

找到案件的犯罪团伙.

Abstract

The terrorist attacks have seriously affected the stability of the international community and the safety of people’s lives and property. The diversification of their forms and means has brought enormous challenges to the analysis of counter-terrorism. In order to classify similar terrorist attacks into groups

and to enhance the efficiency of counter-terrorism analysts in detecting cases

an improved clustering algorithm for Deep Auto-Encoder Representation is proposed based on the Global Terrorism Database. A deep self-encoder is introduced to map sparse and noisy raw data into compact and smooth data within the class

improving the clustering effect. The experimental results show that compared with the traditional K-means clustering algorithm

the improved algorithm can improve the clustering effect. This method is useful for counter-terrorism analysts to analyze similar cases and find criminal gangs in the case.

关键词

Keywords

references

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