Suitability of Clustering Algorithms for Crime Hotspot Analysis

Abstract

Abstract-Crime analysis is a field that needs immediate attention due to the drastic increase of number of crimes. Most of the crimes, from the past experiences of the police, are said to be concentrated in some areas, called hotspots and keep on recurring. Clustering algorithms are best applied to crime analysis, but suitability of broad spectrum of clustering algorithm for an application is an issue to be addressed. In this paper we evaluate three clustering algorithms i.e. hierarchical clustering, k-means clustering and DBSCAN clustering with the intent of finding the best one suitable for crime hotspot analysis. Each one of the clustering algorithm evaluated here need inputs such as number of clusters, neighbour distance, minimum number of points etc. are needed by a cluster. The cluster similarity measure is the Euclidean distance. The results suggest that DBSCAN is much more suitable to crime hotspot analysis due to its inherent nature of being density driven