Cluster evaluation MCQs January 8, 2026November 18, 2024 by u930973931_answers 15 min Score: 0 Attempted: 0/15 Subscribe 1. What is the main goal of cluster evaluation? (A) To find the optimal number of clusters (B) To compute the distance between data points (C) To visualize the clusters in a 2D or 3D space (D) To determine the quality of the clusters formed by a clustering algorithm 2. Which of the following is an internal evaluation metric for clustering? (A) Silhouette Score (B) Rand Index (C) Fowlkes-Mallows Index (D) V-Measure 3. Which of the following is a commonly used external evaluation metric for clustering? (A) Rand Index (B) Silhouette Score (C) Davies-Bouldin Index (D) Within-Cluster Sum of Squares (WCSS) 4. What does the Davies-Bouldin Index measure in clustering evaluation? (A) The average distance between clusters (B) The proportion of points that belong to the same cluster (C) The compactness of the clusters (D) The ratio of the average distance within clusters to the distance between clusters 5. Which of the following is true about the Silhouette Score? (A) The silhouette score ranges from -1 to +1, where a higher score indicates better clustering. (B) A high silhouette score indicates poor clustering. (C) The silhouette score only works for hierarchical clustering. (D) A silhouette score of 0 indicates perfect clustering. 6. The Within-Cluster Sum of Squares (WCSS) is used to evaluate which of the following? (A) The number of clusters (B) The separation between clusters (C) The compactness of the clusters (D) The average distance between points in different clusters 7. What is the main advantage of external evaluation metrics over internal evaluation metrics in clustering? (A) They assess the clustering based on a true class label. (B) They do not require a ground truth. (C) They are less computationally expensive. (D) They work better for high-dimensional data. 8. What does the V-Measure evaluate in clustering? (A) The compactness of clusters (B) The amount of overlap between clusters (C) The similarity of clusters based on their centroid (D) The agreement between the clustering and the true labels 9. What is cluster cohesion? (A) The tightness or compactness of points within the same cluster (B) The distance between the centroids of clusters (C) The distance between different clusters (D) The number of clusters formed 10. What does a high value of the Silhouette Score indicate about the clustering? (A) The clusters are not well-separated. (B) The clustering has many overlaps between clusters. (C) There are many outliers in the dataset. (D) The points are correctly grouped into appropriate clusters. 11. Which of the following clustering evaluation metrics is typically used to compare the clustering result with a ground truth classification? (A) Rand Index (B) Silhouette Score (C) Davies-Bouldin Index (D) Within-Cluster Sum of Squares (WCSS) 12. When evaluating clustering results, what does homogeneity refer to? (A) The degree to which points in the same cluster share the same true label (B) The similarity of clusters to each other (C) The number of points in each cluster (D) The size of the clusters 13. What does the adjusted Rand index (ARI) correct for? (A) The number of points in each cluster (B) The maximum number of clusters possible (C) The distance between cluster centroids (D) The random chance of clustering assignments 14. Which evaluation metric is used to assess how well a clustering algorithm performs in creating clusters of different shapes and sizes? (A) Calinski-Harabasz Index (B) Adjusted Rand Index (C) Silhouette Score (D) Davies-Bouldin Index 15. If the Silhouette Score is close to 0, what does this indicate? (A) The clustering algorithm performed perfectly. (B) The clustering is poor, with points on the border of multiple clusters. (C) The clusters are well-separated and compact. (D) The number of clusters should be increased.