Crime Analysis and Prediction Using Graph Mining

Abstract

Crime investigation and counteractive action is a deliberate methodology for distinguishing and examining examples and patterns in crime. Our framework can foresee regions which have a high likelihood for crime event and can predict crime-prone regions. With the expanding approach of mechanized frameworks, crime information investigators can help the law authorization officers to accelerate the way toward identifying violations. Utilizing the idea of information mining, we can extract beforehand, uncertain valuable data from unstructured information. Crimes are a social aggravation and cost our general public beyond all doubt in a few different ways. Any study that can help in explaining crime quicker will pay for itself. About 10 percent of the criminals carry out about half of the violations. Here we utilize graph mining techniques for gathering information to distinguish the crime instances and accelerate the way toward enlightening crime. Graph mining is done with the help of identifying the structure of the graph to obtain frequent patterns of information. With the help of graph database, we could store the past criminal records and infer important information from it. Our project aims to store the data in a graph database and try to determine the important patterns on the graph which can be used to predict the regions which have a high probability of crime occurrence and can help the law enforcement officers to enhance the speed of the process of solving crimes.