Q. In which scenario would K-means clustering be preferred over hierarchical clustering?
A.
When the number of clusters is unknown
B.
When computational efficiency is a priority
C.
When the data is not well-separated
D.
When a detailed cluster hierarchy is needed
Solution
K-means clustering is preferred when computational efficiency is a priority, especially for large datasets, as it is generally faster than hierarchical clustering.
Correct Answer:
B
— When computational efficiency is a priority
Q. What is the main advantage of using hierarchical clustering?
A.
It is faster than K-means
B.
It does not require the number of clusters to be specified
C.
It can handle large datasets
D.
It is less sensitive to outliers
Solution
A key advantage of hierarchical clustering is that it does not require the number of clusters to be specified in advance, allowing for more flexibility in analysis.
Correct Answer:
B
— It does not require the number of clusters to be specified
Q. Which evaluation metric is often used to assess the quality of clustering?
A.
Accuracy
B.
Silhouette score
C.
F1 score
D.
Mean squared error
Solution
The silhouette score is a common metric used to evaluate the quality of clustering by measuring how similar an object is to its own cluster compared to other clusters.
