Hierarchical Clustering MCQs

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1. : What is the primary objective of Hierarchical Clustering in data mining?
(A) To classify data points into predefined clusters
(B) To find the optimal number of clusters automatically
(C) To reduce the dimensionality of the data
(D) To perform regression analysis

2. : How does Hierarchical Clustering initially treat each data point?
(A) As a separate cluster
(B) By assigning it to the nearest centroid
(C) By randomly assigning it to a cluster
(D) By calculating its distance to all other points

3. : What is the main difference between agglomerative and divisive hierarchical clustering?
(A) Agglomerative starts with all data points in one cluster, while divisive starts with each point as a separate cluster
(B) Agglomerative merges clusters, while divisive splits clusters
(C) Agglomerative uses Euclidean distance, while divisive uses Manhattan distance
(D) Agglomerative is faster than divisive

4. : Which linkage method in hierarchical clustering merges clusters based on the minimum distance between points in each cluster?
(A) Single linkage
(B) Complete linkage
(C) Average linkage
(D) Ward’s linkage

5. : What is the dendrogram used for in hierarchical clustering?
(A) To visualize the data points
(B) To display the distance between clusters at each merge step
(C) To select the optimal number of clusters
(D) To measure the silhouette coefficient

6. : Which method is used to determine the number of clusters in hierarchical clustering?
(A) Silhouette coefficient
(B) Elbow method
(C) Dendrogram
(D) F-measure

7. : What does the “linkage distance” measure in hierarchical clustering?
(A) The distance between data points within a cluster
(B) The distance between centroids of clusters
(C) The distance between clusters at each merge step
(D) The number of iterations until convergence

8. : In hierarchical clustering, what does “Ward’s method” prioritize during cluster merging?
(A) Minimizing the maximum variance within clusters
(B) Minimizing the sum of squared differences within clusters
(C) Minimizing the sum of squared differences between clusters
(D) Maximizing the silhouette coefficient

9. : Which of the following is an advantage of hierarchical clustering?
(A) It requires less computational resources than k-means clustering
(B) It guarantees convergence to the global optimum
(C) It is scalable to large datasets
(D) It does not require specifying the number of clusters in advance

10. : What is a potential drawback of hierarchical clustering?
(A) It is computationally expensive for large datasets
(B) It cannot handle categorical data
(C) It is sensitive to the initial placement of centroids
(D) It requires normalization of data

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Hierarchical Clustering MCQs
By: Prof. Dr. Fazal Rehman | Last updated: May 14, 2025

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