Function density_based_cluster_validity 

pub fn density_based_cluster_validity<F, S1, S2, D>(
    x: &ArrayBase<S1, Ix2>,
    labels: &ArrayBase<S2, D>,
    k: Option<usize>,
) -> Result<F>
where
    F: Float + NumCast + Debug + ScalarOperand + AddAssign + DivAssign,
    S1: Data<Elem = F>,
    S2: Data<Elem = usize>,
    D: Dimension,

Calculate Density-Based Cluster Validity (DBCV) index

The DBCV index measures the validity of a clustering based on the relative density of clusters. It accounts for variations in cluster densities and shapes. Values closer to 1 indicate better clustering.

Arguments

• x - Array of shape (n_samples, n_features) - The data
• labels - Array of shape (n_samples,) - Predicted labels for each sample
• k - Number of neighbors to consider for density calculation (default: 5)

Returns

• DBCV index value (between -1 and 1)

Examples

use scirs2_core::ndarray::{array, Array2};
use scirs2_metrics::clustering::density::density_based_cluster_validity;

// Create a simple dataset with 2 clusters
let x = Array2::from_shape_vec((6, 2), vec![
    1.0, 2.0, 1.5, 1.8, 1.2, 2.2,
    5.0, 6.0, 5.2, 5.8, 5.5, 6.2,
]).unwrap();

let labels = array![0, 0, 0, 1, 1, 1];

let dbcv = density_based_cluster_validity(&x, &labels, None).unwrap();