Research on the Application of Social Network Data Mining Technology in Crime Analysis and Prevention

被引：0
|
作者：
Xiong, Wan Duan [1 ,2 ]
Chong, Wang Yu [1 ,2 ]
机构：
[1] China People's Police University (Guangzhou), Guangdong Province, 399 Aoti South Road, Tianhe District, Guangzhou City,510660, China
[2] China People's Police University, Hebei Provinc, 220 Xichang Road, Anci District, Langfang,065000, China
来源：
Applied Mathematics and Nonlinear Sciences | 2024年 / 9卷 / 01期
关键词：
Social network data mining (SNDM) technology shows great application potential in crime analysis and prevention. This study focuses on revealing the characteristics; laws; and trends of criminal behavior through an in-depth analysis of criminal information in social networks. Using data mining techniques such as association rule mining; cluster analysis; and community discovery; the key information and organizational structure of criminal networks are successfully mined; which provides a powerful means of investigation and prevention for public security departments. It is found that important criminal clues are hidden in the user communication data in social networks; and the communication mode and hidden information between criminals can be revealed through association rule mining technology. Cluster analysis helps to identify gangs and hot spots with similar criminal behaviors; which provides important clues for further investigation. In addition; community discovery technology further reveals the internal structure and membership relationship of criminal gangs; which is helpful in deeply understanding the operation mode of criminal organizations and the spread path of criminal acts. Based on historical data and mining results; this study also constructs a crime trend prediction model; which provides timely early warning information for public security departments and helps to take measures to prevent and crack down on criminal acts in advance. On the whole; this study not only enriches the application theory of SNDM technology in the field of crime analysis but also provides new ideas and tools for actual crime investigation and prevention. © 2024 Wan Duan Xiong and Wang Yu Chong; published by Sciendo;
D O I：
10.2478/amns-2024-1832
中图分类号：
学科分类号：
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