An improved and heuristic-based iterative DBSCAN clustering algorithm

Abstract

In recent years, the clustering of multi-density data has been a research hotspot. As a widely applied clustering algorithm, density-based spatial clustering of application with noise (DBSCAN) algorithm can avoid the interference of noise data, and has the ability to find clusters of any shape. However, the DBSCAN algorithm used hyperparameter (e.g., eps, MinPts), which makes the clusters of different densities cannot be identified. Furthermore, when the algorithm is applied to different datasets, the clustering quality would decrease severely. To cope with the above problems, we propose an improved and heuristic-based iterative DBSCAN clustering algorithm. The new algorithm realizes flexible clustering of the data with different densities by MinPts. In particular, we first estimate the values of MinPts according to the user area densities, then the new clustering is carried out based on the MinPts. This method avoids too dense clusters. In the experiments, Silhouette Coefficient and purity are adopted to evaluate the clustering effect of the improved DBSCAN algorithm. It is demonstrated that the proposed algorithm can effectively cluster multi-density data, and has greater adaptability and clustering performance to all kinds of data.