> ## 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.
# Cluster Quality
> Cluster Quality evaluates how well a clustering algorithm has grouped your data. It calculates metrics like the Silhouette Score, which measures how similar each point is to its own cluster compared to other clusters.
A higher quality score indicates that clusters are well-separated and internally cohesive.
Use Cluster Quality **after** running a clustering algorithm to verify the results. If the score is low, try adjusting the number of clusters or switching to a different clustering method. Combine with the **K-Finder** node to find the optimal number of clusters.
