Description: k-Means partitions data into k clusters based on feature similarity, minimizing the sum of squared distances from each point to the centroid of its assigned cluster.
Key Points:
- Simple and efficient.
- Sensitive to the initial placement of centroids.
- Assumes clusters are spherical.
Applications: Customer segmentation, market research, image compression