An improved density-based spatial clustering of application with noise

Abstract

Although the density-based spatial clustering of application with noise algorithm can identify clusters with arbitrary shape, there is a problem that the global parameter Eps needs to be manually set. In this paper, we propose a parameter adaptive density-based spatial clustering of application with noise by using the cuckoo search algorithm, which could solve the global optimization problem quickly. According to the cuckoo search algorithm to calculate the optimal global parameter Eps, the improved algorithm avoids human intervention in the process of clustering, and achieves clustering process automation. The simulation results show that the proposed algorithm in this paper can select the reasonable Eps parameter value and get the clustering results with high accuracy.