M.I.M.I.R - Multi-agent Intelligent Memory & Insight Repository

# K-Means Clustering **K-Means clustering for vector embeddings (not database clustering).** ## 📚 Documentation - **[K-Means Algorithm](kmeans-algorithm.md)** - Algorithm details and usage - **[Real-Time K-Means](realtime-kmeans.md)** - Live cluster updates - **[GPU Implementation](gpu-implementation.md)** - GPU-accelerated clustering - **[Metal Optimizations](metal-optimizations.md)** - 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 ```cypher // 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](kmeans-algorithm.md)** - How K-Means works - **[GPU Implementation](gpu-implementation.md)** - 10-100x speedup for K-Means - **[Real-Time Updates](realtime-kmeans.md)** - Dynamic K-Means clustering --- **Get started** → **[K-Means Algorithm](kmeans-algorithm.md)**