Q. What is a common challenge when using K-Means clustering?
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A.
It requires labeled data
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B.
Choosing the right number of clusters
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C.
It cannot handle large datasets
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D.
It is sensitive to outliers
Solution
Choosing the right number of clusters (K) is a common challenge in K-Means clustering.
Correct Answer:
B
— Choosing the right number of clusters
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Q. What is the main difference between hierarchical clustering and K-Means clustering?
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A.
Hierarchical clustering requires labeled data
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B.
K-Means clustering is faster
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C.
Hierarchical clustering creates a tree structure
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D.
K-Means clustering can only form circular clusters
Solution
Hierarchical clustering creates a tree structure (dendrogram) to represent the data.
Correct Answer:
C
— Hierarchical clustering creates a tree structure
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Q. Which of the following clustering methods is best suited for discovering clusters of varying shapes and densities?
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A.
K-Means
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B.
DBSCAN
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C.
Agglomerative Clustering
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D.
Gaussian Mixture Models
Solution
DBSCAN is effective for discovering clusters of varying shapes and densities.
Correct Answer:
B
— DBSCAN
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Q. Which of the following clustering methods is best suited for discovering clusters of arbitrary shapes?
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A.
K-Means
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B.
DBSCAN
-
C.
Agglomerative Clustering
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D.
Gaussian Mixture Models
Solution
DBSCAN is effective for discovering clusters of arbitrary shapes and varying densities.
Correct Answer:
B
— DBSCAN
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Q. Which of the following is NOT a type of clustering algorithm?
-
A.
Hierarchical Clustering
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B.
Density-Based Clustering
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C.
K-Nearest Neighbors
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D.
K-Means Clustering
Solution
K-Nearest Neighbors is a classification algorithm, not a clustering algorithm.
Correct Answer:
C
— K-Nearest Neighbors
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