# Module rusty_machine::learning::dbscan
[−]
[src]

DBSCAN Clustering

*Note: This module is likely to change dramatically in the future and
should be treated as experimental.*

Provides an implementaton of DBSCAN clustering. The model
also implements a `predict`

function which uses nearest neighbours
to classify the points. To utilize this function you must use
`self.set_predictive(true)`

before training the model.

The algorithm works by specifying `eps`

and `min_points`

parameters.
The `eps`

parameter controls how close together points must be to be
placed in the same cluster. The `min_points`

parameter controls how many
points must be within distance `eps`

of eachother to be considered a cluster.

If a point is not within distance `eps`

of a cluster it will be classified
as noise. This means that it will be set to `None`

in the clusters `Vector`

.

# Examples

use rusty_machine::learning::dbscan::DBSCAN; use rusty_machine::learning::UnSupModel; use rusty_machine::linalg::Matrix; let inputs = Matrix::new(6, 2, vec![1.0, 2.0, 1.1, 2.2, 0.9, 1.9, 1.0, 2.1, -2.0, 3.0, -2.2, 3.1]); let mut model = DBSCAN::new(0.5, 2); model.train(&inputs).unwrap(); let clustering = model.clusters().unwrap();

## Structs

DBSCAN |
DBSCAN Model |