A novel adaptive density-based spatial clustering of application with noise based on bird swarm optimization algorithm

The commonly used density-based spatial clustering method (DBSCAN) connects contiguous regions with sufficiently large densities when processing datasets to efficiently discover clusters of different shapes and densities and outliers. However, the algorithm has the problem that radius of neighborhood (Eps) argument requires to be selected manually. For datasets with higher dimensionality and larger data volume, the selection of Eps parameters can be difficult thus leading to poor clustering quality. To solve the above problem, we propose a novel adaptive density-based spatial clustering of application with noise based on bird swarm optimization algorithm (BSA-DBSCAN). We use the global search capability of the bird swarm method to select the best Eps parameter neighborhood values. We can avoid manual intervention and realize adaptive parameter optimization in the clustering process. To further explore the clustering performance of BSA-DBSCAN method, we test the synthetic datasets and the real-world datasets respectively and perform images analysis on the clustering evaluation index values. The simulation experiments show that the improved method in this paper can reasonably search the Eps parameter value and can obtain the higher accuracy of clustering.