How DBSCAN Clustering Works
DBSCAN discovers clusters of arbitrary shape based on point density. It automatically identifies noise points and doesn't require specifying the number of clusters.
About the Customer Segments Dataset
200 synthetic customer records with spending, income, and loyalty data. Perfect for market segmentation with clustering.
- Samples
- 200
- Features
- 5
- 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 DBSCAN Clustering
DBSCAN 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 Customer Segments
See K-Means Clustering applied to the Customer Segments dataset (200 samples, 5 features). Interactive visualization, metrics, and analysis.
K-Medoids Clustering on Customer Segments
See K-Medoids Clustering applied to the Customer Segments dataset (200 samples, 5 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.
DBSCAN Clustering on Wine Quality
See DBSCAN Clustering applied to the Wine Quality dataset (178 samples, 13 features). Interactive visualization, metrics, and analysis.