Risk Assessment of Terrorism in Asia based on K-Means Clustering Analysis

Abstract

This article obtains terrorist attack data from some regions of the global terrorism database, and uses Python language to preprocess and describe the data. Cluster analysis is performed on the preprocessed data using the K-means algorithm. During the analysis process, the focus is on the two key variables of casualties and economic losses, in order to find the patterns and trends behind the data. Based on the clustering results, the level of danger in each region was scientifically graded to assist counter-terrorism forces in gaining a deeper understanding of the core information of terrorist attacks in these areas, thereby enhancing their awareness of terrorist attacks and enhancing their ability to prevent them.