Design of computationally efficient density-based clustering algorithms

• Proposed a new strategy to reduce computational complexity associated with the DBSCAN
• Developed new density based algorithms based on correlation measure
• Cluster analysis on two synthetic and six real datasets demonstrates the performance of proposed method.
• An interesting application is demonstrated to identify the regional hazard regions present in seismic catalog of Japan.

The basic DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm uses minimum number of input parameters, very effective to cluster large spatial databases but involves more computational complexity. The present paper proposes a new strategy to reduce the computational complexity associated with the DBSCAN by efficiently implementing new merging criteria at the initial stage of evolution of clusters. Further new density based clustering (DBC) algorithms are proposed considering correlation coefficient as similarity measure. These algorithms though computationally not efficient, found to be effective when there is high similarity between patterns of dataset. The computations associated with DBC based on correlation algorithms are reduced with new cluster merging criteria. Test on several synthetic and real datasets demonstrates that these computationally efficient algorithms are comparable in accuracy to the traditional one. An interesting application of the proposed algorithm has been demonstrated to identify the regional hazard regions present in the seismic catalog of Japan.