Mini toolsDefinition
An iterative clustering algorithm that partitions data into K clusters by minimizing within-cluster distances.
Detailed Explanation
K-means assigns points to the nearest cluster center and updates centers based on cluster means. It iterates until convergence, minimizing the sum of squared distances within clusters. The algorithm requires specifying K beforehand and is sensitive to initial center placement and outliers.
Use Cases
1. Market segmentation 2. Image compression 3. Document classification 4. Pattern recognition
