DBSCAN Calculator

Here you can perform a DBSCAN clustering analysis online. Just select your variables and click DBSCAN.

DBSCAN

DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a density-based clustering algorithm that groups points in high-density regions into clusters and labels points in sparse regions as noise. It can discover arbitrarily shaped clusters using just two parameters: ε (neighborhood radius) and minPoints (minimum density).

DBSCAN groups points based on local density, automatically detecting arbitrarily shaped clusters and labeling low-density points as noise without requiring a predefined number of clusters. The k-means calculator, by contrast, partitions all points into a fixed number of roughly spherical clusters around centroids, assigning every point to the nearest center and lacking an explicit noise model.