Density-based spatial clustering of applications with noise (DBSCAN) is a data clustering algorithm proposed by Martin Ester, Hans-Peter Kriegel, Jörg Sander and Xiaowei Xu in 1996.It is a ...

DBSCAN is a classical density-based clustering algorithm, which is widely used for data clustering analysis due to its simple and efficient characteristics. The purpose of this paper is to study ...

The extension allows you to perform unsupervised density-based clustering of turtles/agents and patches based on specified variables or by proximity. The main advantage over supervised algorithms such ...

They both outshine DBSCAN and other clustering algorithms in finding clusters ... HDBSCAN is preferable if you want a simple and stable set of clusters, while OPTICS is better for a flexible ...

To solve this problem, this paper proposed an improved multi-scale dense crowd detection method based on YOLOv5 and improved the DBSCAN clustering algorithm ... Figure 1 is a flowchart of the improved ...

DBSCAN is a classical density-based clustering algorithm, which is widely used for data clustering analysis due to its simple and efficient characteristics. The purpose of this paper is to study ...