> ## 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.
# Spectral Clustering
> Spectral Clustering uses a similarity graph and its mathematical properties (eigenvalues) to discover complex, non-convex cluster shapes that traditional algorithms like K-Means cannot handle.
It is particularly effective when clusters have irregular, non-spherical boundaries.
Spectral Clustering excels at finding clusters with **non-convex shapes** (e.g., L-shaped, crescent-shaped regions). If K-Means fails to capture the natural grouping in your data, try Spectral Clustering.
### Use Cases
* **Complex spatial patterns**: When building layouts or urban patterns form non-convex shapes (e.g., L-shaped building clusters, ring-shaped arrangements).
* **Connectivity-based grouping**: Group geometries based on their adjacency relationships rather than just distance.
* **Irregular cluster shapes**: When K-Means produces unsatisfactory results because clusters are not spherical.
