K-Means Clustering
K-Means partitions data into K clusters minimizing the within-cluster sum of squared distances, alternating between assigning points to the nearest center and updating centers to cluster means.
Purpose
To provide the most widely used partitioning clustering algorithm, simple and efficient.
Mechanism
Initialize K centers. Repeat: 1) assign each point to its nearest center; 2) update each center to the mean of its cluster. It converges to a local optimum (the objective decreases monotonically). K-Means++ improves initialization.
Use cases
- Data clustering
- Image segmentation
- Vector quantization