Validation of Clustering Solutions

Validation of clustering solutions involves assessing how well the grouping reflects meaningful patterns in the data. It helps determine if the clusters are consistent, distinct, and relevant to the underlying data. Techniques include internal validation metrics (like silhouette score), which measure how closely related items in the same cluster are compared to those in different clusters, and external validation, comparing clusters to known labels if available. The goal is to ensure the clustering results are reliable and useful, providing confidence that the groups truly represent underlying structures rather than random divisions.