> ## Documentation Index
> Fetch the complete documentation index at: https://shapely.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.
# Gaussian Mixture
> Gaussian Mixture Model (GMM) assumes data comes from a mix of Gaussian distributions, each representing a cluster. Unlike K-Means, it calculates the probability of a point belonging to each cluster.
This "soft clustering" approach is ideal when cluster boundaries are not clearly defined or overlap.
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
