Crime Analysis Using K-Means Clustering

Abstract

Analysis of crime is essential for providing safety and security to the civilian population. Using data mining, we can discover critical information which can help local authorities detect crime and areas of importance. The main purpose of this paper is to analyze the crime which entails theft, homicide and various drug offences which also include suspicious activities, noise complaints and burglar alarm by using qualitative and quantitative approach. Using K-means clustering data mining approach on a crime dataset from New South Wales region of Australia, crime rates of each type of crimes and cities with high crime rates have been found.