Enum ClusterAlgorithm 

pub enum ClusterAlgorithm {
    KMeans {
        k: usize,
        max_iter: u32,
        tolerance: f64,
    },
    MiniBatchKMeans {
        k: usize,
        batch_size: usize,
        max_iter: u32,
    },
    DBSCAN {
        eps: f64,
        min_samples: usize,
    },
    Agglomerative {
        k: usize,
        linkage: Linkage,
    },
}

Which clustering algorithm the SemanticClusterer should use.

Variants

KMeans

Standard KMeans with KMeans++ seeding.

Fields

k: usize

Number of clusters to form.

max_iter: u32

Maximum number of EM iterations.

tolerance: f64

Centroid-shift tolerance for early stopping.

MiniBatchKMeans

Mini-batch variant of KMeans for large datasets.

Fields

k: usize

Number of clusters.

batch_size: usize

Points sampled per iteration.

max_iter: u32

Maximum iterations.

DBSCAN

Density-Based Spatial Clustering of Applications with Noise.

Fields

eps: f64

Neighbourhood radius.

min_samples: usize

Minimum neighbours (including self) to be a core point.

Agglomerative

Hierarchical agglomerative clustering.

Fields

k: usize

Target number of clusters.

linkage: Linkage

Linkage criterion.

Trait Implementations

impl Clone for ClusterAlgorithm

fn clone(&self) -> ClusterAlgorithm

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for ClusterAlgorithm

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

Formats the value using the given formatter.

impl Display for ClusterAlgorithm

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

Formats the value using the given formatter.