DBSCAN MCQs – T4Tutorials.com

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1. : What is DBSCAN primarily used for in data mining?
(A) Regression analysis
(B) Clustering spatial data
(C) Dimensionality reduction
(D) Classification

2. : How does DBSCAN determine the core points in a dataset?
(A) By calculating the mean of all data points
(B) By measuring the distance to the nearest centroid
(C) By counting the number of points within a specified radius (ε)
(D) By applying principal component analysis

3. : What does the ε parameter control in DBSCAN?
(A) The number of clusters to form
(B) The minimum number of points required to form a cluster
(C) The maximum distance between two points to be considered neighbors
(D) The number of iterations for convergence

4. : What is the significance of the MinPts parameter in DBSCAN?
(A) It defines the maximum radius for clustering
(B) It determines the number of clusters
(C) It specifies the minimum number of points required to form a dense region
(D) It measures the density of each point

5. : How does DBSCAN handle noise points in a dataset?
(A) By assigning noise points to the nearest cluster
(B) By removing noise points from the dataset
(C) By ignoring noise points during clustering
(D) By treating noise points as a separate cluster

6. : Which of the following statements about DBSCAN is true?
(A) It requires the number of clusters to be known in advance
(B) It is sensitive to the order of data points
(C) It can only handle numerical data
(D) It uses a hierarchical approach for clustering

7. : What is the primary advantage of DBSCAN compared to k-means clustering?
(A) It is faster and more scalable for large datasets
(B) It guarantees convergence to the global optimum
(C) It does not require the number of clusters to be specified beforehand
(D) It handles non-linear relationships between data points

8. : Which type of clusters can DBSCAN effectively identify?
(A) Clusters of equal sizes
(B) Clusters with arbitrary shapes and sizes
(C) Clusters with high-dimensional data
(D) Clusters with categorical data

9. : What is a key limitation of DBSCAN?
(A) It is sensitive to noise and outliers
(B) It requires a large number of iterations for convergence
(C) It cannot handle high-dimensional data
(D) It is computationally expensive for large datasets

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

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