Application of Improved Clustering Algorithm in Terrorist Attacks

He Qing-xiang; Zhang Wei

doi:10.12052/gdutxb.190052

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Application of Improved Clustering Algorithm in Terrorist Attacks

更新时间：2026-05-27

- Application of Improved Clustering Algorithm in Terrorist Attacks
- Vol. 36, Issue 4, Pages: 24-30(2019)
- 作者机构：
  
  广东工业大学计算机学院,广东,广州,510006
- 作者简介：
- 基金信息：
- DOI：10.12052/gdutxb.190052
  CLC： TP391
- Online First：18 June 2019，
  
  Published：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.

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Keywords

references

李国辉. 全球恐怖袭击时空演变及风险分析研究[D].合肥: 中国科学技术大学,2014

TENG S H, WU H, LI R G, et al . The application of the adjustable multi-times clustering algorithm in telecom data [J ] . Journal of Guangdong University of technology, 2014, 31(3) : 1-7.

ALTMAN I, HUNTER M M. The employment and income effects of cleaner coal: the case of future gen and rural Illinois [J]. Clean Technologies and Environmental Policy, 2015, 17(6): 1475-1485.

BARALIS E, CAGLIERO L, CERQUITELLI T, et al . Digging deep into weighted patient data through multiple-level patterns [J ] . Information Sciences, 2015, 322: 51-71.

WU S Z. Potential terrorist gang discovery technology in counter-terrorism intelligence based on cluster analysis [J]. Police Technology, 2016(6): 18-21.

ZHANG W, MAI Z S. Research on LOF noise reduction method based on kernel fuzzy spectral clustering [J]. Journal of Guangdong University of Technology, 2018, 35(6): 77-82.

LIBRARY W E. National consortium for the study of terrorism and responses to terrorism[C]// International Symposium on High-power Laser Ablation. Santa Fe, NM: International Society for Optics and Photonics, 2010.

PETER K, MICHAEL D I, JOHN P S. Countering terrorism and WMD: creating a global counter-terrorism network[M]. New York: Routledge, 2006.

CHEN H, REID E, SINAI J, et al. Terrorism informatics: knowledge management and data mining for homeland security[M]. West Berlin: Springer Publishing Company, 2008.

WANG X, MILLER E, SMARICK K, et al . Investigative visual analysis of global terrorism [J ] . Computer Graphics Forum, 2008, 27(3): 919-926.

JONES J, BUTKIEWICZ T, RIBARSKY W. Visualizing uncertainty for geographical information in the global terrorism database [J]. Proceedings of SPIE - The International Society for Optical Engineering, 2008, 15(9): 1396.

颜峻. 基于时空数据挖掘的社会安全(刑事)事件成因研究[D]. 北京清华大学, 2009.

彭如香, 张奥博, 杨涛, 等. 基于GTD的全球恐怖主义活动现状与发展趋势研究[J]. 计算机应用与软件, 2019, 36(1): 1-5.

LI Y N, MEI J M. Application of ROCK clustering in anti-terrorism intelligence analysis [J]. Journal of Information, 2017(10): 26-29.

AYTEKIN C, NI X, CRICRI F, et al. Clustering and unsupervised anomaly detection with L2 normalized deep auto-encoder representations [J]. arXiv preprint arXiv: 1802.00187, 2018.

CHANG C J, DAI W L. A grey silhouette coefficient for the small sample forecasting[C]// IEEE International Conference on Grey Systems Intelligent Services. Macau: IEEE, 2014.

LUKASIK S, KOWALSKI P A, CHARYTANOWICZ M, et al. Clustering using flower pollination algorithm and calinski-harabasz index[C]// 2016 IEEE Congress on Evolutionary Computation. Vancouver: IEEE, 2016.

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