An Adaptive Clustering Method Based on Improved DBSCAN

Density-based Spatial Clustering of Applications with Noise (DBSCAN) is a widely used clustering algorithm based on density measures; however, it performs poorly on multi-density datasets. Additionally, it necessitates two key input parameters: the radius (Eps) and the minimum number of points (MinPts), both of which need to be experimentally determined for accuracy. To address these issues, this paper proposes an adaptive clustering method based on an improved DBSCAN, which determines parameter pair based on the data distribution. The method does not require pre-set parameters and can effectively cluster multi-density datasets. In order to assess the efficacy of the proposed method, experiments were carried out using synthetic datasets and the results were compared with those of the conventional DBSCAN algorithm. The experimental results demonstrate that the proposed method can successfully cluster multi-density datasets without the need for manual parameter adjustment.