Density-Based Spatial Clustering of Applications with Noise is a clustering algorithm able to find non-linearly separable and arbitrarily-shaped clusters given a radius and density constraint. In addition, DBSCAN also has the ability to mark outliers as noise and thus can be used as a quasi anomaly detector.

Note: The smaller the radius, the tighter the clusters will be.

Note: Noise samples are assigned the cluster number -1.

Interfaces: Estimator

Data Type Compatibility: Continuous


# Param Default Type Description
1 radius 0.5 float The maximum distance between two points to be considered neighbors.
2 min density 5 int The minimum number of points within radius of each other to form a cluster.
3 tree BallTree object The spatial tree used to run range searches.

Additional Methods#

This estimator does not have any additional methods.


use Rubix\ML\Clusterers\DBSCAN;
use Rubix\ML\Graph\Trees\BallTree;
use Rubix\ML\Kernels\Distance\Diagonal;

$estimator = new DBSCAN(4.0, 5, new BallTree(20, new Diagonal()));


  • M. Ester et al. (1996). A Densty-Based Algorithm for Discovering Clusters.