K-Means Clustering¶

K-Means clustering for vector embeddings (not database clustering).

📚 Documentation¶

• K-Means Algorithm - Algorithm details and usage
• Real-Time K-Means - Live cluster updates
• GPU Implementation - GPU-accelerated clustering
• Metal Optimizations - Apple Silicon fixes

🎯 What is K-Means Clustering?¶

K-Means clustering groups similar vectors together, enabling: - Faster approximate search - Data organization - Anomaly detection - Dimensionality reduction

🚀 Quick Start¶

// Create clusters from embeddings
CALL nornicdb.cluster.kmeans({
  k: 10,
  maxIterations: 100,
  tolerance: 0.001
})
YIELD clusterId, centroid, size
RETURN clusterId, size

📖 Learn More¶

• K-Means Algorithm - How K-Means works
• GPU Implementation - 10-100x speedup for K-Means
• Real-Time Updates - Dynamic K-Means clustering

Get started → K-Means Algorithm