GMM can model clusters with different shapes, sizes, and orientations. While K-Means assumes all clusters are spherical and equally sized, GMM provides much more flexibility in representing real-world data patterns.
Use Cases
- Probabilistic assignment: When you need to know the probability that a geometry belongs to each group, rather than a hard assignment.
- Heterogeneous clusters: When clusters in your data have different shapes and densities