How OPTICS Clustering Works
OPTICS creates a reachability plot that reveals hierarchical cluster structure at multiple density levels, without fixing a single epsilon parameter.
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 OPTICS Clustering
OPTICS 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 Customer Segments
See DBSCAN Clustering applied to the Customer Segments dataset (200 samples, 5 features). Interactive visualization, metrics, and analysis.
HDBSCAN Clustering on Customer Segments
See HDBSCAN Clustering applied to the Customer Segments dataset (200 samples, 5 features). Interactive visualization, metrics, and analysis.