Validation of cluster analysis is crucial to ensure the reliability of the results. Common validation techniques include:
Silhouette analysis: This measures how similar an object is to its own cluster compared to other clusters. Higher silhouette values indicate better clustering. Davies-Bouldin index: This index considers the ratio of within-cluster scatter to between-cluster separation. Lower values indicate better clustering. Cross-validation: This involves dividing the data into subsets, performing clustering on each subset, and comparing the results to assess consistency.
