How HDBSCAN Clustering Works
HDBSCAN extends DBSCAN to handle varying-density clusters. It builds a hierarchy and extracts the most stable clusters automatically.
About the Iris Flowers Dataset
Classic 150-sample dataset with 4 petal/sepal measurements across 3 species. The gold standard for clustering & classification demos.
- Samples
- 150
- Features
- 4
- Type
- Numeric
- Category
- Density-based
Key Metrics to Watch
Silhouette Score
Measures how similar a point is to its own cluster vs. other clusters. Ranges from −1 to +1; higher is better.
Calinski-Harabasz Index
Ratio of between-cluster to within-cluster variance. Higher values indicate denser, well-separated clusters.
Davies-Bouldin Index
Average similarity between each cluster and its most similar cluster. Lower is better.
Inertia (Within-Cluster SSE)
Sum of squared distances from each point to its assigned centroid. Lower indicates tighter clusters.
When to Use HDBSCAN Clustering
HDBSCAN Clustering belongs to the Density-based family of clustering algorithms. These methods identify clusters as regions of high point density separated by regions of low density. They can discover clusters of arbitrary shape.
Related Examples
K-Means Clustering on Iris Flowers
See K-Means Clustering applied to the Iris Flowers dataset (150 samples, 4 features). Interactive visualization, metrics, and analysis.
K-Medoids Clustering on Iris Flowers
See K-Medoids Clustering applied to the Iris Flowers dataset (150 samples, 4 features). Interactive visualization, metrics, and analysis.
DBSCAN Clustering on Iris Flowers
See DBSCAN Clustering applied to the Iris Flowers dataset (150 samples, 4 features). Interactive visualization, metrics, and analysis.
HDBSCAN Clustering on Wine Quality
See HDBSCAN Clustering applied to the Wine Quality dataset (178 samples, 13 features). Interactive visualization, metrics, and analysis.