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# 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](../estimator.md)

**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
```php
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
[^1]: M. Ester et al. (1996). A Density-Based Algorithm for Discovering Clusters.

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