DBSCAN#
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
Parameters#
| # | 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.
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()));
References#
- M. Ester et al. (1996). A Densty-Based Algorithm for Discovering Clusters.