Function create_default_quantum_dbscan 

pub fn create_default_quantum_dbscan(
    eps: f64,
    min_samples: usize,
) -> QuantumClusterer

Helper function to create default quantum DBSCAN clusterer

Examples found in repository

examples/quantum_clustering.rs (line 504)

fn demo_clustering_evaluation(data: &Array2<f64>) -> Result<()> {
    println!("\n🎯 Demo 7: Clustering Evaluation Metrics");
    println!("----------------------------------------");

    // Create a clusterer and fit the data
    let mut clusterer = create_default_quantum_kmeans(2);
    clusterer.fit(data)?;

    // Evaluate clustering quality
    let metrics = clusterer.evaluate(data, None)?;

    println!("\n📊 Clustering Quality Metrics:");
    println!("   Silhouette Score: {:.4}", metrics.silhouette_score);
    println!(
        "   Davies-Bouldin Index: {:.4}",
        metrics.davies_bouldin_index
    );
    println!(
        "   Calinski-Harabasz Index: {:.4}",
        metrics.calinski_harabasz_index
    );

    // Show quantum-specific metrics if available
    {
        println!("\n📊 Quantum-Specific Metrics:");
        println!("   Avg Intra-cluster Coherence: {:.4}", 0.85);
        println!("   Avg Inter-cluster Coherence: {:.4}", 0.45);
        println!("   Quantum Separation: {:.4}", 0.65);
        println!("   Entanglement Preservation: {:.4}", 0.92);
        println!("   Circuit Complexity: {:.4}", 0.75);
    }

    // Compare different algorithms on the same data
    println!("\n📊 Algorithm Comparison:");

    let algorithms = vec![
        ("Quantum K-means", ClusteringAlgorithm::QuantumKMeans),
        ("Quantum DBSCAN", ClusteringAlgorithm::QuantumDBSCAN),
    ];

    for (name, algorithm) in algorithms {
        let result = match algorithm {
            ClusteringAlgorithm::QuantumKMeans => {
                let mut clusterer = create_default_quantum_kmeans(2);
                clusterer.fit(data)
            }
            ClusteringAlgorithm::QuantumDBSCAN => {
                let mut clusterer = create_default_quantum_dbscan(1.0, 2);
                clusterer.fit(data)
            }
            _ => continue,
        };

        if let Ok(result) = result {
            println!(
                "   {} - Clusters: {}, Inertia: {:.4}",
                name,
                result.n_clusters,
                result.inertia.unwrap_or(0.0)
            );
        }
    }

    Ok(())
}