A Parallel Crime Activity Clustering Algorithm Based on Apache Spark Cloud Computing Platform

Khin Nandar Win, Jianguo Chen, Guoqing Xiao, Yuedan Chen, Philippe Fournier-Viger

Abstract: Nowadays, there are more and more criminal behaviors experiencing around the world, and crime spiking has become one of the most critical security and social issues in almost every country. It is critical to seek effective ways to discover these criminal behaviors and patterns and to carry out the prevention for the target place. In this paper, we formulate the problem of criminal behaviors and propose a Criminal Activity Clustering (CAC) algorithm. We introduce the fuzzy clustering method to detect potential criminal patterns in large-scale spatiotemporal datasets. In addition, for the improvement of the the proposed CAC algorithm performance, we implement a parallel solution for the algorithm in the Apache Spark cloud computing platform. The results of the experiment show that the proposed CAC algorithm can effectively detect accurate criminal patterns from large-scale spatiotemporal data.