A collection of clustering algorithms. Currently this crate provides DBSCAN, HDBSCAN and OPTICS.
The following example shows how to cluster points using DBSCAN.
use ndarray::array;
use petal_clustering::{Dbscan, Fit};
let points = array![[1., 2.], [2., 2.], [2., 2.3], [8., 7.], [8., 8.], [25., 80.]];
let clustering = Dbscan::new(3.0, 2).fit(&points);
assert_eq!(clustering.0.len(), 2); // two clusters found
assert_eq!(clustering.0[&0], [0, 1, 2]); // the first three points in Cluster 0
assert_eq!(clustering.0[&1], [3, 4]); // [8., 7.] and [8., 8.] in Cluster 1
assert_eq!(clustering.1, [5]); // [25., 80.] doesn't belong to any clusterCopyright 2019-2023 Petabi, Inc.
Licensed under Apache License, Version 2.0 (the "License"); you may not use this crate except in compliance with the License.
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