Density-based clustering (e.g., DBSCAN) MCQs January 8, 2026November 18, 2024 by u930973931_answers 15 min Score: 0 Attempted: 0/15 Subscribe 1. What is the main idea behind DBSCAN (Density-Based Spatial Clustering of Applications with Noise)? (A) Cluster data based on density of points in the dataset (B) Cluster data based on the distance between data points (C) Partition data into predefined K clusters (D) Minimize the variance within clusters 2. Which of the following parameters is required to run the DBSCAN algorithm? (A) The number of clusters (K) (B) The number of iterations (C) The distance metric (D) The radius of the neighborhood (epsilon, ε) and the minimum number of points (minPts) 3. What is the role of the epsilon (ε) parameter in DBSCAN? (A) It controls the number of iterations the algorithm runs (B) It specifies the number of clusters to form (C) It defines the maximum distance between two points to be considered part of the same cluster (D) It defines the number of outliers in the dataset 4. What is the significance of the minPts parameter in DBSCAN? (A) It specifies the number of features in each cluster (B) It defines the minimum number of clusters to be created (C) It specifies the number of outliers in the dataset (D) It defines the minimum number of data points required to form a cluster 5. Which of the following points in DBSCAN is considered a “core point”? (A) A point that is located at the center of the cluster (B) A point that is closer to the cluster centroid than others (C) A point with at least minPts points in its epsilon neighborhood (D) A point that has no neighboring points within epsilon distance 6. In DBSCAN, what is a “border point”? (A) A point that lies at the centroid of a cluster (B) A point with no points in its epsilon neighborhood (C) A point with fewer than minPts points in its epsilon neighborhood but is in the neighborhood of a core point (D) A point that is considered an outlier 7. What is an “outlier” or “noise” point in DBSCAN? (A) A point that does not have enough points within its epsilon neighborhood to be considered a part of any cluster (B) A point that is located at the center of a cluster (C) A point that has the highest distance from the cluster centroid (D) A point that is always at the edge of a cluster 8. What is the main advantage of DBSCAN over K-means clustering? (A) DBSCAN requires the number of clusters to be specified in advance. (B) DBSCAN always produces fewer clusters than K-means. (C) DBSCAN can detect clusters of arbitrary shape, while K-means can only detect spherical clusters. (D) DBSCAN does not require distance metrics. 9. Which of the following is a limitation of DBSCAN? (A) It cannot detect clusters of arbitrary shape. (B) It is sensitive to the choice of epsilon (ε) and minPts parameters. (C) It does not require a distance metric. (D) It always produces a fixed number of clusters. 10. Which data structure is typically used in DBSCAN to optimize the search for neighboring points? (A) Hash table (B) KD-tree (C) Decision tree (D) Adjacency matrix 11. What happens if the epsilon (ε) parameter in DBSCAN is set too large? (A) More points will be classified as outliers. (B) The algorithm will form fewer, larger clusters. (C) The algorithm will not detect any clusters. (D) The algorithm will form more, smaller clusters. 12. What happens if the epsilon (ε) parameter in DBSCAN is set too small? (A) The algorithm will detect many small clusters. (B) The algorithm will merge all points into one cluster. (C) More points will be classified as outliers. (D) The algorithm will not work properly. 13. How does DBSCAN handle noise or outliers in the data? (A) DBSCAN ignores noise points completely. (B) DBSCAN assigns noise points to the closest cluster. (C) DBSCAN treats noise points as a separate cluster. (D) DBSCAN labels noise points as “outliers” and does not assign them to any cluster. 14. Which type of data is DBSCAN particularly well-suited for? (A) Data with clusters of varying shapes and densities (B) High-dimensional data (C) Data with a fixed number of clusters (D) Data with categorical variables 15. What does the “minPts” parameter influence in DBSCAN? (A) The maximum number of clusters formed (B) The number of points required to form a cluster (C) The size of each cluster (D) The density of points in a cluster