1. What is DBSCAN primarily used for in data mining?
a) Regression analysis
b) Clustering spatial data
c) Dimensionality reduction
d) Classification
Answer: b) Clustering spatial data
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
Answer: c) By counting the number of points within a specified radius (ε)
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
Answer: c) The maximum distance between two points to be considered neighbors
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
Answer: c) It specifies the minimum number of points required to form a dense region
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
Answer: 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
Answer: b) It is sensitive to the order of data points
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
Answer: c) It does not require the number of clusters to be specified beforehand
8. What is the computational complexity of DBSCAN?
a) O(n log n)
b) O(n^2)
c) O(n)
d) O(n*k)
Answer: b) O(n^2), where n is the number of data points.
9. 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
Answer: b) Clusters with arbitrary shapes and sizes
10. 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
Answer: a) It is sensitive to noise and outliers
More Next Data Mining MCQs
- Repeated Data Mining MCQs
- Classification in Data mining MCQs
- Clustering in Data mining MCQs
- Data Analysis and Experimental Design MCQs
- Basics of Data Science MCQs
- Big Data MCQs
- Caret Data Science MCQs
- Binary and Count Outcomes MCQs
- CLI and Git Workflow
- Data Preprocessing MCQs
- Data Warehousing and OLAP MCQs
- Association Rule Learning MCQs
- Classification
- Clustering
- Regression MCQs
- Anomaly Detection MCQs
- Text Mining and Natural Language Processing (NLP) MCQs
- Web Mining MCQs
- Sequential Pattern Mining MCQs
- Time Series Analysis MCQs
Data Mining Algorithms and Techniques MCQs
- Frequent Itemset Mining MCQs
- Dimensionality Reduction MCQs
- Ensemble Methods MCQs
- Data Mining Tools and Software MCQs
- Python Programming for Data Mining MCQs (Pandas, NumPy, Scikit-Learn)
- R Programming for Data Mining(dplyr, ggplot2, caret) MCQs
- SQL Programming for Data Mining for Data Mining MCQs
- Big Data Technologies MCQs