Police Data Mining and Classification Based on PDCSC Algorithm in International Police Cooperation

Aiming at the problems of low data collaboration rate and weak storage capacity of police surveillance data in national border police cooperation, the research is based on Parallel Distributed Clustering with Semantic Constraints (PDCSC) algorithm. Police surveillance data processing model is constructed. The research firstly utilizes local density clustering algorithm to mine and classify the surveillance data, and then introduces the concept of parallelism and constructs the processing model using PDCSC algorithm. The results show that the clustering purity of police surveillance data based on PDCSC method is 92.37% and the data clustering accuracy is 93.67%. Meanwhile, the surveillance data summary of PDCSC method is 7108 which is 1085 and 2241 higher than that of DBSCAN and K-means. This indicates that the PDCSC algorithm can effectively process and classify police surveillance data, providing accurate police incident identification and analysis. The research aims to provide strong support for international police cooperation and improve the efficiency and accuracy of police work.