DBSCANalgoritmen

DBSCAN stands for Density-Based Spatial Clustering of Applications with Noise. It is a popular unsupervised machine learning algorithm used for data clustering. The core idea behind DBSCAN is to group together points that are closely packed together (points with many nearby neighbors) and mark as outliers points that lie alone in low-density regions.

The algorithm defines two key parameters: epsilon (eps) and a minimum number of points (minPts). Epsilon is the maximum distance between two samples for one to be considered as in the neighborhood of the other. MinPts is the number of samples in a neighborhood for a point to be considered as a core point. This includes the point itself.

DBSCAN classifies points into three types: core points, border points, and noise points. A point is a core point if it has at least minPts points (including itself) within its eps neighborhood. A point is a border point if it is not a core point but lies within the eps neighborhood of a core point. Any point that is neither a core point nor a border point is classified as a noise point.

The algorithm works by starting with an arbitrary unvisited point and, if it is a core point, initiating a new cluster. It then iteratively adds all points reachable from the core point to the cluster. Reachability is defined based on density connectivity. Points are considered density-connected if there is a chain of core points leading from one to another. If the starting point is not a core point, it is marked as noise and will be considered later. The process continues until all points have been visited. A significant advantage of DBSCAN is its ability to discover clusters of arbitrary shapes and its robustness to outliers.