Crate anofox_ml_cluster 

Clustering algorithms: K-Means and DBSCAN.

This crate provides unsupervised clustering methods for grouping data points:

• KMeans – Lloyd’s algorithm with k-means++ initialization.
• Dbscan – density-based spatial clustering of applications with noise.

Both implement FitUnsupervised and Predict, so the fitted model can assign cluster labels to new data points.

Examples

use ndarray::array;
use anofox_ml_core::{FitUnsupervised, Predict};
use anofox_ml_cluster::KMeans;

let x = array![
    [0.0, 0.0],
    [1.0, 0.0],
    [0.0, 1.0],
    [10.0, 10.0],
    [11.0, 10.0],
    [10.0, 11.0]
];

let kmeans = KMeans::new(2).with_seed(42);
let fitted = FitUnsupervised::<f64>::fit(&kmeans, &x).unwrap();

// Points in the same group get the same label
let labels = fitted.labels();
assert_eq!(labels[0] as usize, labels[1] as usize);
assert_eq!(labels[3] as usize, labels[4] as usize);
assert_ne!(labels[0] as usize, labels[3] as usize);

Re-exports

pub use affinity_propagation::AffinityPropagation;

pub use affinity_propagation::FittedAffinityPropagation;

pub use agglomerative::AgglomerativeClustering;

pub use agglomerative::FittedAgglomerativeClustering;

pub use agglomerative::Linkage;

pub use bgmm::BayesianGaussianMixture;

pub use bgmm::FittedBayesianGaussianMixture;

pub use birch::Birch;

pub use birch::FittedBirch;

pub use dbscan::Dbscan;

pub use dbscan::FittedDbscan;

pub use gmm::CovarianceType;

pub use gmm::FittedGaussianMixture;

pub use gmm::GaussianMixture;

pub use hdbscan::FittedHdbscan;

pub use hdbscan::Hdbscan;

pub use kmeans::FittedKMeans;

pub use kmeans::KMeans;

pub use mean_shift::FittedMeanShift;

pub use mean_shift::MeanShift;

pub use mini_batch_kmeans::FittedMiniBatchKMeans;

pub use mini_batch_kmeans::MiniBatchKMeans;

pub use optics::FittedOptics;

pub use optics::Optics;

pub use spectral::Affinity;

pub use spectral::FittedSpectralClustering;

pub use spectral::SpectralClustering;

Modules

affinity_propagation

Affinity Propagation.

agglomerative

Agglomerative (hierarchical) clustering.

bgmm

Bayesian Gaussian Mixture Model — variational inference.

birch

Birch-lite — single-pass online sub-clustering, final global KMeans.

dbscan

gmm

Gaussian Mixture Model with EM training.

hdbscan

HDBSCAN — Hierarchical Density-Based Spatial Clustering.

kmeans

mean_shift

Mean-Shift clustering.

mini_batch_kmeans

Mini-batch K-Means.

optics

OPTICS — Ordering Points To Identify the Clustering Structure.

spectral

Spectral clustering.