MCQs

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Overall MCQs for Reference

1. What is the primary aim of hierarchical clustering in artificial intelligence?
   a. To perform regression analysis
   b. To represent data in a tree structure
   c. To group similar data points based on hierarchy
   d. To evaluate the accuracy of classification models
   Answer: c. To group similar data points based on hierarchygeeksforgeeks+1​
2. Which of the following best describes a dendrogram?
   a. A scatter plot for clusters
   b. A tree-like diagram showing cluster relationships
   c. A histogram of cluster sizes
   d. A bar chart representing centroid values
   Answer: b. A tree-like diagram showing cluster relationshipsbradleyboehmke.github+1​
3. In hierarchical clustering, what are the two main approaches?
   a. Agglomerative and divisive
   b. Supervised and unsupervised
   c. Linear and nonlinear
   d. Flat and dynamic
   Answer: a. Agglomerative and divisivegeeksforgeeks+1​
4. Agglomerative clustering is also known as which approach?
   a. Top-down
   b. Bottom-up
   c. Random
   d. Center-out
   Answer: b. Bottom-upgeeksforgeeks​
5. Divisive hierarchical clustering starts with:
   a. Each data point as a cluster
   b. A single cluster containing all data points
   c. All clusters having one data point
   d. Randomly assigned clusters
   Answer: b. A single cluster containing all data pointsgeeksforgeeks​
6. Which linkage method combines clusters based on the closest pair of points?
   a. Complete linkage
   b. Average linkage
   c. Single linkage
   d. Ward linkage
   Answer: c. Single linkageexploredatabase+1​
7. Complete linkage in hierarchical clustering uses which criterion?
   a. Minimum distance
   b. Maximum distance
   c. Average distance
   d. Centroid distance
   Answer: b. Maximum distanceresearchhubs+1​
8. The process of re-calculating distances when merging clusters is called:
   a. Cluster normalization
   b. Distance updating
   c. Matrix transformation
   d. Distance matrix update
   Answer: d. Distance matrix updategeeksforgeeks​
9. What is a common stopping criterion for hierarchical clustering?
   a. When all data points are in one cluster
   b. After a fixed number of iterations
   c. When a predefined distance is reached
   d. When clusters reach a minimum size
   Answer: a. When all data points are in one clustergeeksforgeeks​
10. Which hierarchical clustering method cannot assign an object to more than one cluster?
    a. Fuzzy clustering
    b. K-means clustering
    c. Agglomerative clustering
    d. Gaussian mixture model
    Answer: c. Agglomerative clusteringgeeksforgeeks+1​
11. What kind of data structure is naturally suited for storing the results of hierarchical clustering?
    a. Array
    b. Linked list
    c. Tree
    d. Graph
    Answer: c. Treebradleyboehmke.github+1​
12. Which of the following is NOT a typical linkage criterion?
    a. Single linkage
    b. Multiple linkage
    c. Complete linkage
    d. Average linkage
    Answer: b. Multiple linkageexploredatabase+1​
13. Which hierarchical clustering technique is more computationally expensive?
    a. Agglomerative
    b. Divisive
    c. Both are equal
    d. None
    Answer: b. Divisivegeeksforgeeks​
14. Which type of clustering requires pre-specification of the number of clusters?
    a. K-means
    b. Divisive hierarchical
    c. Agglomerative hierarchical
    d. DBSCAN
    Answer: a. K-meanssanfoundry+1​
15. The lowest level in a dendrogram indicates:
    a. Maximum similarity
    b. Minimum similarity
    c. Random grouping
    d. Centroid cluster
    Answer: a. Maximum similaritybradleyboehmke.github​
16. Hierarchical clustering is considered a type of:
    a. Supervised learning
    b. Reinforcement learning
    c. Unsupervised learning
    d. Semi-supervised learning
    Answer: c. Unsupervised learninggeeksforgeeks​
17. In hierarchical clustering, the merges are typically visualized using:
    a. Regression lines
    b. Dendrograms
    c. Scatter plots
    d. Box plots
    Answer: b. Dendrogramsbradleyboehmke.github+1​
18. When applying agglomerative clustering, the first step is:
    a. Merging the largest clusters
    b. Starting with each data point as its own cluster
    c. Assigning classes based on centroids
    d. Deleting outliers
    Answer: b. Starting with each data point as its own clusterexploredatabase+1​
19. Which metric is NOT commonly used for distance calculation in hierarchical clustering?
    a. Euclidean distance
    b. Manhattan distance
    c. Jaccard distance
    d. Cosine similarity
    Answer: d. Cosine similaritygeeksforgeeks​
20. Which of the following is an advantage of hierarchical clustering?
    a. Speed with large datasets
    b. No need to predefine the number of clusters
    c. Requires labeled data
    d. Minimizes outlier effects
    Answer: b. No need to predefine the number of clustersbradleyboehmke.github+1​
21. Linkage criteria are used to determine:
    a. How clusters are initialized
    b. How distances between clusters are measured
    c. How outliers are handled
    d. How centroids are computed
    Answer: b. How distances between clusters are measuredresearchhubs+1​
22. The output of hierarchical clustering is typically:
    a. A collection of cluster centroids
    b. A hierarchical tree (dendrogram)
    c. A list of feature importances
    d. A decision tree
    Answer: b. A hierarchical tree (dendrogram)bradleyboehmke.github+1​
23. Which is true regarding outliers in hierarchical clustering?
    a. They are automatically removed
    b. They can severely impact the hierarchy
    c. They are merged first
    d. They have no influence
    Answer: b. They can severely impact the hierarchybradleyboehmke.github​
24. Divisive hierarchical clustering is sometimes performed using:
    a. Principal Component Analysis
    b. K-means splitting
    c. Random forest
    d. Decision tree classification
    Answer: b. K-means splittinggeeksforgeeks​
25. What is the complexity of agglomerative clustering for n data points?
    a. O(n log n)
    b. O(n^2)
    c. O(log n)
    d. O(1)
    Answer: b. O(n^2)geeksforgeeks+1​
26. Hierarchical clustering is most suitable for:
    a. Large datasets
    b. Text classification
    c. Small to medium datasets
    d. Linear regression
    Answer: c. Small to medium datasetsbradleyboehmke.github+1​
27. The process of merging the most similar clusters repeatedly until only one cluster is left is called:
    a. Divisive clustering
    b. Agglomerative clustering
    c. Association mining
    d. Centroid splitting
    Answer: b. Agglomerative clusteringaimcqs+1​
28. Which method uses average distance between all pairs in two clusters as a linkage criterion?
    a. Single linkage
    b. Centroid linkage
    c. Complete linkage
    d. Average linkage
    Answer: d. Average linkageexploredatabase+1​
29. Ward’s method in hierarchical clustering aims to minimize:
    a. Excluded variance
    b. Maximum distance
    c. Total within-cluster variance
    d. Outlier influence
    Answer: c. Total within-cluster variancegeeksforgeeks​
30. What is “cutting the dendrogram” used for in hierarchical clustering?
    a. Assigning initial centroids
    b. Defining cluster memberships
    c. Removing outliers
    d. Improving speed
    Answer: b. Defining cluster membershipsbradleyboehmke.github+1​
31. Which approach is more sensitive to noise and outliers in hierarchical clustering?
    a. Agglomerative
    b. Divisive
    c. Both equally sensitive
    d. None
    Answer: a. Agglomerativegeeksforgeeks​
32. In hierarchical clustering, merging stops when:
    a. Only one cluster remains
    b. Distance exceeds a threshold
    c. Predefined number of clusters reached
    d. Any of the above
    Answer: d. Any of the abovegeeksforgeeks​
33. Which algorithm builds clusters by successively dividing a large cluster?
    a. Agglomerative
    b. Divisive
    c. K-means
    d. Mean Shift
    Answer: b. Divisivegeeksforgeeks​
34. Which is false regarding dendrograms?
    a. They visualize the merging process
    b. They provide a hierarchy of clusters
    c. They always provide optimal clustering
    d. They help in selecting cluster level
    Answer: c. They always provide optimal clusteringbradleyboehmke.github​
35. Which cluster analysis is not hierarchical?
    a. K-means
    b. Agglomerative
    c. Divisive
    d. Complete linkage
    Answer: a. K-meanssanfoundry+1​
36. Which function in scikit-learn is commonly used for hierarchical clustering?
    a. KMeans
    b. AgglomerativeClustering
    c. DBSCAN
    d. GaussianMixture
    Answer: b. AgglomerativeClusteringgeeksforgeeks​
37. Hierarchical clustering produces clusters at:
    a. Single resolution
    b. Multiple resolutions
    c. Random levels
    d. No levels
    Answer: b. Multiple resolutionsgeeksforgeeks​
38. In divisive clustering, the dataset is repeatedly:
    a. Merged
    b. Split
    c. Shuffled
    d. Labeled
    Answer: b. Splitgeeksforgeeks​
39. Hierarchical clustering is least effective when:
    a. The data has hierarchical relationships
    b. There are a small number of well-separated clusters
    c. The number of objects is very large
    d. Objects have similar features
    Answer: c. The number of objects is very largebradleyboehmke.github+1​
40. Complete linkage usually produces clusters that are:
    a. Elongated
    b. Compact and spherical
    c. Arbitrarily shaped
    d. Not well separated
    Answer: b. Compact and sphericalresearchhubs​
41. What is the main risk of using single linkage clustering?
    a. Chains of loosely related points (“chaining”)
    b. Overfitting clusters
    c. Overlapping clusters
    d. Missing centroids
    Answer: a. Chains of loosely related points (“chaining”)researchhubs​
42. In hierarchical clustering, the choice of distance metric:
    a. Has little effect
    b. Affects the shape of the dendrogram
    c. Is not needed
    d. Only affects speed
    Answer: b. Affects the shape of the dendrogrambradleyboehmke.github+1​
43. Hierarchical clustering is commonly used for which of the following tasks?
    a. Feature selection
    b. Exploratory data analysis
    c. Regression
    d. Dimensionality reduction
    Answer: b. Exploratory data analysisbradleyboehmke.github+1​
44. The process of grouping similar items without prior labels in hierarchical clustering is called:
    a. Classification
    b. Clustering
    c. Visualization
    d. Forecasting
    Answer: b. Clusteringgeeksforgeeks​
45. Which is a disadvantage of hierarchical clustering?
    a. Cannot produce dendrograms
    b. Requires labeled data
    c. High computational complexity for large datasets
    d. Provides only one possible clustering
    Answer: c. High computational complexity for large datasetsbradleyboehmke.github+1​
46. In agglomerative clustering, the similarity between clusters is updated after:
    a. Each split
    b. Each merge
    c. Each iteration of K-means
    d. Each new data point
    Answer: b. Each mergegeeksforgeeks​
47. Which application might use hierarchical clustering?
    a. Document categorization
    b. Linear regression
    c. Time-series forecasting
    d. Model tuning
    Answer: a. Document categorizationbradleyboehmke.github+1​
48. What is the key visual difference between clusters formed by single linkage vs. complete linkage?
    a. Single linkage forms chains; complete linkage forms compact clusters
    b. Both form identical clusters
    c. Single linkage forms compact clusters; complete linkage forms chains
    d. There is no visual difference
    Answer: a. Single linkage forms chains; complete linkage forms compact clustersresearchhubs​
49. Hierarchical clustering results are often interpreted by:
    a. Examining centroids
    b. Inspecting the dendrogram for cluster selection
    c. Plotting residuals
    d. Counting data points
    Answer: b. Inspecting the dendrogram for cluster selectionbradleyboehmke.github+1​
50. Which is true about the hierarchical clustering process?
    a. All decisions are reversible
    b. Each merge/split is final and irreversible
    c. Assignments can be changed at any time
    d. Splits and merges are done randomly
    Answer: b. Each merge/split is final and irreversiblebradleyboehmke.github+1​

1. https://www.geeksforgeeks.org/quizzes/hierarchical-clustering/
2. https://www.geeksforgeeks.org/machine-learning/hierarchical-clustering/
3. https://bradleyboehmke.github.io/HOML/hierarchical.html
4. https://www.exploredatabase.com/2022/02/machine-learning-mcq-hierarchical-agglomerative-clustering-single-and-complete-linkage.html
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6. https://www.sanfoundry.com/clustering-methods-k-means-dbscan-hierarchical/
7. https://aimcqs.com/clustering
8. https://www.aionlinecourse.com/ai-quiz-questions/machine-learning/hierarchical-clustering
9. https://www.sanfoundry.com/data-science-questions-answers-clustering/
10. https://www.aionlinecourse.com/ai-quiz-questions/machine-learning/hierarchical-clustering/quiz-
11. https://360digitmg.com/blog/hierarchical-clustering-interview-questions-answers
12. https://www.youtube.com/watch?v=bjlIf0zb0Zs
13. https://www.scribd.com/document/660661097/5Clustering-Solved-MCQs-of-Clustering-in-Data-mining-with-Answers
14. https://www.scribd.com/document/889063866/Short-Questions-for-Hierarchical-Clustering-1