DBSCAN DensityBased Spatial Clustering Application

DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a popular clustering algorithm used in data mining and machine learning. It is particularly well-suited for identifying clusters of arbitrary shapes in spatial data. Developed by Martin Ester, Hans-Peter Kriegel, Jörg Sander, and Xiaowei Xu in 1996, DBSCAN has become widely adopted due to its ability to discover clusters of varying shapes and sizes, as well as its capacity to handle noise effectively. Here we want to give an introduction to this algorithm with an practical example in Python.