Q. How can clustering be applied in anomaly detection?
-
A.
By identifying outliers in data
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B.
By predicting future values
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C.
By classifying data into categories
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D.
By optimizing resource allocation
Solution
Clustering can be applied in anomaly detection by identifying outliers that do not fit well into any cluster.
Correct Answer:
A
— By identifying outliers in data
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Q. In which field is clustering used for image segmentation?
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A.
Finance
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B.
Healthcare
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C.
Computer Vision
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D.
Natural Language Processing
Solution
Clustering is widely used in computer vision for image segmentation, helping to identify and separate different objects within an image.
Correct Answer:
C
— Computer Vision
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Q. What is a key challenge when applying clustering algorithms?
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A.
Choosing the right number of clusters
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B.
Data normalization
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C.
Feature selection
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D.
All of the above
Solution
Choosing the right number of clusters, data normalization, and feature selection are all key challenges when applying clustering algorithms.
Correct Answer:
D
— All of the above
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Q. What is a primary benefit of using clustering in social network analysis?
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A.
Identifying influential users
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B.
Predicting future trends
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C.
Enhancing user privacy
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D.
Improving data storage
Solution
Clustering helps identify influential users in social networks by grouping users with similar behaviors or connections.
Correct Answer:
A
— Identifying influential users
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Q. What is the role of clustering in bioinformatics?
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A.
Predicting protein structures
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B.
Grouping similar genes or proteins
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C.
Classifying diseases
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D.
Enhancing data visualization
Solution
In bioinformatics, clustering is used to group similar genes or proteins based on their expression patterns.
Correct Answer:
B
— Grouping similar genes or proteins
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Q. Which clustering algorithm is often used for customer segmentation?
-
A.
K-Means
-
B.
Linear Regression
-
C.
Decision Trees
-
D.
Support Vector Machines
Solution
K-Means is a popular clustering algorithm used for customer segmentation due to its efficiency and simplicity.
Correct Answer:
A
— K-Means
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Q. Which clustering method is best for large datasets with noise?
-
A.
K-Means
-
B.
DBSCAN
-
C.
Agglomerative Clustering
-
D.
Gaussian Mixture Models
Solution
DBSCAN is effective for large datasets with noise as it can identify clusters of varying shapes and sizes while ignoring outliers.
Correct Answer:
B
— DBSCAN
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Q. Which clustering method is suitable for discovering natural groupings in data?
-
A.
Hierarchical Clustering
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B.
Linear Regression
-
C.
Random Forest
-
D.
Naive Bayes
Solution
Hierarchical clustering is suitable for discovering natural groupings in data by creating a tree of clusters.
Correct Answer:
A
— Hierarchical Clustering
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Q. Which clustering technique is best for large datasets with noise?
-
A.
K-Means
-
B.
DBSCAN
-
C.
Agglomerative Clustering
-
D.
Gaussian Mixture Models
Solution
DBSCAN is effective for large datasets with noise as it can identify clusters of varying shapes and sizes while ignoring outliers.
Correct Answer:
B
— DBSCAN
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Q. Which clustering technique is suitable for discovering natural groupings in data?
-
A.
Hierarchical Clustering
-
B.
Linear Regression
-
C.
Random Forest
-
D.
Naive Bayes
Solution
Hierarchical clustering is suitable for discovering natural groupings in data by creating a tree-like structure of clusters.
Correct Answer:
A
— Hierarchical Clustering
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Q. Which of the following is NOT a typical application of clustering?
-
A.
Market segmentation
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B.
Document classification
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C.
Image compression
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D.
Time series forecasting
Solution
Time series forecasting is not a typical application of clustering; it is more related to supervised learning.
Correct Answer:
D
— Time series forecasting
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