Modified DBSCAN Using Particle Swarm Optimization for Spatial Hotspot Identification

Spatial hotspots of irregular shape occur naturally in fields like epidemiology and earth science. Classical techniques for identifying hotspots are either based on scan statistics or clustering algorithms. These techniques result in hotspots of fixed shapes like circle, ellipse or straight line. Density based spatial clustering of applications with noise (DBSCAN) is one of the often used algorithms for finding non-geometric shaped clusters. It is highly sensitive to the values of its input variables (MinPoints and Epsilon) which are to be provided by the users. In this paper, we propose a Particle Swarm Optimization (PSO) based approach which automatically computes the values of MinPoints and Epsilon for given input data and finds the spatial hotspots. The modified DBSCAN approach is applied to six artificial datasets and purity of the resultant clustering is calculated. Achieved values of the purity function indicate the accuracy of the proposed method. Proposed approach is also applied to find out the hotspots for earthquake zoning.