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.