Example Concept: One common pitfall in evaluating clustering algorithms is relying solely on internal validation metrics like the Silhouette Score or Davies-Bouldin Index without considering the context or domain-specific knowledge. These metrics measure the compactness and separation of clusters but may not reflect the true quality of clustering if the data has complex structures or noise. Additionally, using a single metric can overlook other important aspects such as cluster interpretability or business relevance.