Clustering Techniques MCQs January 8, 2026November 18, 2024 by u930973931_answers 15 min Score: 0 Attempted: 0/15 Subscribe 1. Which of the following is a characteristic of clustering? (A) It is a type of supervised learning. (B) It is a type of unsupervised learning. (C) It requires labeled data. (D) It involves predictions based on historical data. 2. What does the K-means algorithm aim to minimize? (A) The number of clusters (B) The total number of data points (C) The distance between clusters (D) The variance within each cluster 3. In the K-means clustering algorithm, what does āKā represent? (A) The number of features in the dataset (B) The number of nearest neighbors (C) The number of clusters to form (D) The number of iterations 4. What is the main disadvantage of K-means clustering? (A) It requires labeled data for training. (B) It cannot handle large datasets. (C) It works only with numeric data. (D) It is sensitive to the initial placement of the centroids. 5. Which of the following clustering algorithms does NOT require the number of clusters to be specified beforehand? (A) K-means clustering (B) Both B and C (C) Hierarchical clustering (D) DBSCAN 6. In DBSCAN, what does the term āepsilonā (Īµ) refer to? (A) The distance threshold for considering points as neighbors (B) The maximum number of clusters that can be formed (C) The number of iterations to run (D) The density of the data 7. What is the main goal of hierarchical clustering? (A) To partition the data into a predefined number of clusters (B) To minimize the within-cluster variance (C) To group data into a tree-like structure of nested clusters (D) To maximize the distance between clusters 8. Which of the following clustering algorithms is best suited for detecting outliers? (A) K-means clustering (B) DBSCAN (C) K-medoids (D) Agglomerative clustering 9. In K-means clustering, what happens if āKā is set too high? (A) The algorithm converges more quickly. (B) The model underfits, creating large clusters. (C) The model overfits, creating very small clusters. (D) The clusters become less informative. 10. Which of the following methods is used to determine the optimal number of clusters in K-means clustering? (A) All of the above (B) Silhouette score (C) Gap statistic (D) Elbow method 11. What is the main difference between K-means and K-medoids clustering? (A) K-medoids requires a predefined number of clusters, while K-means does not. (B) K-means is faster than K-medoids. (C) K-medoids can only be used for numeric data, while K-means can handle categorical data. (D) K-means uses the mean of the data points in each cluster, while K-medoids uses the actual data points (medoids) as cluster centers. 12. What type of data is DBSCAN best suited for? (A) Data with noise and varying densities (B) Data that is linearly separable (C) Large datasets with few outliers (D) Data with a fixed number of clusters 13. Which clustering technique is most appropriate for data that follows a tree-like structure? (A) Hierarchical clustering (B) DBSCAN (C) K-means (D) Gaussian mixture models 14. What is the āSilhouette Scoreā used for in clustering? (A) To measure the average size of the clusters (B) To find the optimal number of clusters for K-means (C) To measure how similar each point is to its own cluster compared to other clusters (D) To determine the number of features in the dataset 15. In hierarchical clustering, which of the following methods merges clusters based on the shortest distance between any two points in the clusters? (A) Wardās method (B) Complete linkage (C) Average linkage (D) Single linkage