Module clustering 

Clustering metrics module

This module provides functions for evaluating clustering algorithms, including silhouette score, Davies-Bouldin index, Calinski-Harabasz index, and Adjusted Rand index.

Internal Metrics

Internal metrics assess clustering without external ground truth:

• Silhouette score
• Davies-Bouldin index
• Calinski-Harabasz index
• Dunn index
• Inter-cluster and intra-cluster distance metrics

External Metrics

External metrics assess clustering compared to ground truth:

• Adjusted Rand index
• Normalized Mutual Information
• Adjusted Mutual Information
• Homogeneity, Completeness, V-measure
• Fowlkes-Mallows score

Re-exports

pub use self::evaluation::dunn_index_enhanced;

pub use self::evaluation::elbow_method;

pub use self::density::*;

pub use self::distance::*;

pub use self::external_metrics::*;

pub use self::validation::*;

Modules

density

Density-based metrics for clustering evaluation

distance

Cluster distance metrics module

evaluation

Clustering evaluation utilities

external_metrics

External clustering metrics module

validation

Specialized clustering validation metrics

Structs

SilhouetteAnalysis

Structure containing detailed silhouette analysis results

Functions

calinski_harabasz_score

Calculates the Calinski-Harabasz index (Variance Ratio Criterion)

davies_bouldin_score

Calculates the Davies-Bouldin index for a clustering

dunn_index

Calculates the Dunn index for a clustering

silhouette_analysis

Calculates detailed silhouette information for a clustering

silhouette_samples

Calculate silhouette samples for a clustering

silhouette_score

Calculates the silhouette score for a clustering

silhouette_scores_per_cluster

Calculate silhouette scores per cluster