Hierarchical clustering MCQs January 8, 2026November 18, 2024 by u930973931_answers 15 min Score: 0 Attempted: 0/15 Subscribe 1. What is the primary objective of hierarchical clustering? (A) To partition data into a predefined number of clusters (B) To minimize the number of clusters (C) To classify data into categories based on predefined labels (D) To form a hierarchy of clusters from the dataset 2. Which of the following is true about agglomerative hierarchical clustering? (A) It starts with a single cluster containing all data points. (B) It starts with each data point in its own cluster. (C) It merges clusters until only one remains. (D) It does not require a distance metric. 3. What is the main difference between agglomerative and divisive hierarchical clustering? (A) Agglomerative starts with one large cluster, while divisive starts with individual points. (B) Agglomerative is a bottom-up approach, while divisive is a top-down approach. (C) Divisive clustering uses a predefined number of clusters, while agglomerative does not. (D) Agglomerative is more computationally efficient than divisive. 4. In hierarchical clustering, what is a dendrogram? (A) A method for calculating the optimal number of clusters (B) A data transformation technique used before clustering (C) A visual representation of the hierarchy of clusters (D) A measure of the distances between clusters 5. Which of the following linkage methods is based on the minimum distance between points in different clusters? (A) Average linkage (B) Complete linkage (C) Single linkage (D) Ward’s linkage 6. What does “complete linkage” refer to in hierarchical clustering? (A) The sum of distances between clusters (B) The average distance between points in different clusters (C) The minimum distance between points in different clusters (D) The maximum distance between points in different clusters 7. Which of the following methods is NOT typically used to calculate the distance between clusters in hierarchical clustering? (A) Jaccard similarity (B) Manhattan distance (C) Cosine similarity (D) Euclidean distance 8. What is Ward’s linkage method in hierarchical clustering? (A) It minimizes the variance within each cluster. (B) It maximizes the distance between clusters. (C) It uses the nearest point to merge clusters. (D) It is not based on any distance metric. 9. How does the computational complexity of hierarchical clustering compare to K-means clustering? (A) Hierarchical clustering is less efficient than K-means. (B) Hierarchical clustering is more efficient than K-means. (C) Both algorithms have the same computational complexity. (D) Hierarchical clustering has lower space complexity than K-means. 10. Which distance metric is most commonly used in hierarchical clustering? (A) Minkowski distance (B) Manhattan distance (C) Euclidean distance (D) Hamming distance 11. What does “average linkage” in hierarchical clustering compute? (A) The average distance between the nearest points in two clusters (B) The maximum distance between any point in one cluster and any point in the other cluster (C) The minimum distance between any point in one cluster and any point in the other cluster (D) The average distance between all points in two clusters 12. Which of the following is true about hierarchical clustering? (A) It requires the number of clusters to be specified before running the algorithm. (B) It can be computationally expensive for large datasets. (C) It always produces the same results regardless of the linkage method used. (D) It cannot be visualized. 13. Which of the following clustering algorithms does not require the number of clusters to be predefined? (A) Both B and C (B) DBSCAN (C) Agglomerative hierarchical clustering (D) K-means clustering 14. In hierarchical clustering, which of the following linkage methods is most likely to produce compact, spherical clusters? (A) Single linkage (B) Complete linkage (C) Ward’s linkage (D) Average linkage 15. Which of the following is NOT an advantage of hierarchical clustering? (A) It is easy to visualize with dendrograms. (B) It does not require a predefined number of clusters. (C) It is computationally efficient for large datasets. (D) It can produce a hierarchy of clusters.