How Fuzzy C-Means Clustering Works
Fuzzy C-Means allows points to belong to multiple clusters with varying degrees of membership, capturing overlapping cluster boundaries.
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
- Probabilistic
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 Fuzzy C-Means Clustering
Fuzzy C-Means Clustering belongs to the Probabilistic family of clustering algorithms. These methods model each cluster as a probability distribution, providing soft assignments and uncertainty estimates.
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.