Journal of Guangdong University of Technology ›› 2019, Vol. 36 ›› Issue (04): 24-30.doi: 10.12052/gdutxb.190052

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Application of Improved Clustering Algorithm in Terrorist Attacks

He Qing-xiang, Zhang Wei   

  1. School of Computers, Guangdong University of Technology, Guangzhou 510006, China
  • Received:2019-04-01 Online:2019-06-18 Published:2019-05-31

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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.

Key words: terrorist attack, deep auto-encoder representation, clustering

CLC Number: 

  • TP391
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