Struct AgglomerativeClustering 

pub struct AgglomerativeClustering { /* private fields */ }

Agglomerative (bottom-up) hierarchical clustering.

Builds a tree of clusters (dendrogram) by iteratively merging closest clusters using various linkage methods.

Algorithm

1. Start with each point as its own cluster
2. Repeat until reaching n_clusters:
   • Find two closest clusters using linkage method
   • Merge them
   • Update distance matrix
3. Return cluster labels

Examples

use aprender::prelude::*;

let data = Matrix::from_vec(6, 2, vec![
    1.0, 1.0, 1.1, 1.0, 1.0, 1.1,
    5.0, 5.0, 5.1, 5.0, 5.0, 5.1,
]).expect("Valid matrix dimensions and data length");

let mut hc = AgglomerativeClustering::new(2, Linkage::Average);
hc.fit(&data).expect("Fit succeeds with valid data");

let labels = hc.predict(&data);
assert_eq!(labels.len(), 6);

Performance

• Time complexity: O(n³) for naive implementation
• Space complexity: O(n²) for distance matrix

Implementations

impl AgglomerativeClustering

pub fn new(n_clusters: usize, linkage: Linkage) -> Self

Create new AgglomerativeClustering with target number of clusters and linkage method.

pub fn n_clusters(&self) -> usize

Get target number of clusters.

pub fn linkage(&self) -> Linkage

Get linkage method.

pub fn is_fitted(&self) -> bool

Check if model has been fitted.

pub fn labels(&self) -> &Vec<usize>

Get cluster labels (panic if not fitted).

pub fn dendrogram(&self) -> &Vec<Merge>

Get dendrogram merge history (panic if not fitted).

Trait Implementations

impl Clone for AgglomerativeClustering

fn clone(&self) -> AgglomerativeClustering

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for AgglomerativeClustering

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl<'de> Deserialize<'de> for AgglomerativeClustering

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl Serialize for AgglomerativeClustering

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.

impl UnsupervisedEstimator for AgglomerativeClustering

type Labels = Vec<usize>

The type of labels/clusters produced.

fn fit(&mut self, x: &Matrix<f32>) -> Result<()>

Fits the model to data.

fn predict(&self, _x: &Matrix<f32>) -> Self::Labels

Predicts cluster assignments or transforms data.