petal-clustering

A collection of clustering algorithms. Currently this crate provides DBSCAN, HDBSCAN and OPTICS.

Examples

The following example shows how to cluster points using DBSCAN.

use ndarray::array;
use petal_neighbors::distance::Euclidean;
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., 2, Euclidean::default()).fit(&points, None);

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 cluster

License

Copyright 2019-2026 Petabi, Inc.

Licensed under Apache License, Version 2.0 (the "License"); you may not use this crate except in compliance with the License.