DBSCAN#
Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is a clustering algorithm able to find non-linearly separable and arbitrarily-shaped clusters given a radius and density constraint. In addition, DBSCAN can flag outliers (noise samples) and thus be used as a quasi-anomaly detector.
Note
Noise samples are assigned the cluster number -1.
Interfaces: Estimator
Data Type Compatibility: Depends on distance kernel
Parameters#
# | Name | Default | Type | Description |
---|---|---|---|---|
1 | radius | 0.5 | float | The maximum distance between two points to be considered neighbors. |
2 | minDensity | 5 | int | The minimum number of points within radius of each other to form a cluster. |
3 | tree | BallTree | Spatial | The spatial tree used to run range searches. |
Example#
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()));
Additional Methods#
This estimator does not have any additional methods.
References#
-
M. Ester et al. (1996). A Density-Based Algorithm for Discovering Clusters. ↩