Clustering MCQs January 8, 2026November 18, 2024 by u930973931_answers 15 min Score: 0 Attempted: 0/15 Subscribe 1. What is the primary goal of clustering in machine learning? (A) To group similar data points into clusters based on their attributes (B) To classify data into predefined categories (C) To reduce the size of the dataset (D) To predict continuous values based on input features 2. Which of the following is a popular algorithm for partitional clustering? (A) DBSCAN (B) Gaussian Mixture Models (GMM) (C) Agglomerative hierarchical clustering (D) K-means 3. In K-means clustering, how is the number of clusters (k) typically determined? (A) Through trial and error or methods like the elbow method (B) It is determined by the algorithm itself (C) By applying a decision tree (D) By using cross-validation 4. Which of the following is a key step in the K-means algorithm? (A) Finding the global minimum of a cost function (B) Calculating the hierarchical tree structure of the data (C) Assigning data points to the closest cluster centroid (D) Calculating the probability distribution of data points 5. What does the Silhouette score measure in clustering? (A) The accuracy of clustering (B) The compactness and separation of clusters (C) The number of clusters in the dataset (D) The entropy of the clusters 6. Which of the following is a density-based clustering algorithm? (A) K-means (B) DBSCAN (C) Gaussian Mixture Models (GMM) (D) Agglomerative hierarchical clustering 7. In DBSCAN, what does epsilon (ε) represent? (A) The maximum number of points in a cluster (B) The average distance between points in a cluster (C) The minimum number of clusters (D) The distance threshold for determining whether points are neighbors 8. What is the main advantage of hierarchical clustering over K-means? (A) It works well for high-dimensional data (B) It is computationally more efficient (C) It does not require the number of clusters to be specified in advance (D) It can handle missing values more effectively 9. Which type of clustering is agglomerative hierarchical clustering? (A) Partitional (B) Density-based (C) Divisive (D) Hierarchical 10. What is the main difference between K-means and K-medoids clustering? (A) K-means uses centroids as cluster centers, while K-medoids uses actual data points (B) K-means works only with numerical data, while K-medoids can handle categorical data (C) K-means is a density-based algorithm, while K-medoids is partitional (D) K-means is used for hierarchical clustering, while K-medoids is for partitional clustering 11. Which of the following is a limitation of the K-means clustering algorithm? (A) It can handle non-linear boundaries between clusters (B) It is sensitive to the initial placement of centroids (C) It is efficient for high-dimensional data (D) It does not require the number of clusters to be predefined 12. What does the elbow method help determine in clustering? (A) The most optimal clustering algorithm to use (B) The minimum number of clusters (C) The distance between centroids (D) The best number of clusters (k) for the dataset 13. What does the DBSCAN algorithm require as input? (A) The number of clusters (B) A list of class labels for the data points (C) A distance measure (epsilon) and minimum points in a cluster (D) A predefined hierarchical tree 14. In Gaussian Mixture Models (GMM), how are clusters represented? (A) As overlapping groups of data points with probabilistic membership (B) As rigid boundaries around each data point (C) By assigning a cluster label to each data point (D) As hierarchical trees of data points 15. Which of the following is a weakness of K-means clustering? (A) It works well on categorical data (B) It can handle non-convex clusters well (C) It requires fewer computational resources than DBSCAN (D) It assumes clusters are spherical and of similar size