  Selecting an appropriate clustering method as well as an optimal number of
clusters in road accident data is at times confusing and difficult. This paper
analyzes shortcomings of different existing techniques applied to cluster
accident-prone areas and recommends using Density-Based Spatial Clustering of
Applications with Noise (DBSCAN) and Ordering Points To Identify the Clustering
Structure (OPTICS) to overcome them. Comparative performance analysis based on
real-life data on the recorded cases of road accidents in North Carolina also
show more effectiveness and efficiency achieved by these algorithms.
