- Centroid vs. Medoid:
- K-means: Uses centroids (mean points), it is sensitive to outliers.
- K-medoids: Uses medoids (minimizes distances), less sensitive to outliers.
- Robustness to Outliers:
- K-means: Sensitive to outliers.
- K-medoids: More robust to outliers.
- Initialization:
- K-means: Sensitive to initial centroids.
- K-medoids: Less sensitive to initial medoids.
- Cluster Shape:
- K-means: Assumes spherical clusters.
- K-medoids: Handles arbitrary shapes.
- Computational Complexity(cost) :
- K-means: Less computationally expensive.
- K-medoids: Can be more computationally expensive.
- Cluster Connectivity:
- K-means: Does not naturally connect non-contiguous regions.
- K-medoids: Connects points based on density.
- Use Cases:
- K-means: Well-defined, spherical clusters, computational efficiency.
- K-medoids: Irregular clusters, robustness to outliers.
Finally, k-means clustering is faster, suitable for spherical clusters, and sensitive to outliers.
K-medoids is more robust to outliers, handles arbitrary shapes, but can be computationally more expensive.