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 |