Q. In hierarchical clustering, what does the dendrogram represent?
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A.
The accuracy of the model
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B.
The hierarchy of clusters
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C.
The distance between data points
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D.
The number of features
Solution
A dendrogram visually represents the arrangement of clusters in hierarchical clustering.
Correct Answer:
B
— The hierarchy of clusters
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Q. In the context of clustering, what does 'curse of dimensionality' refer to?
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A.
The increase in computational cost with more dimensions
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B.
The difficulty in visualizing high-dimensional data
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C.
The sparsity of data in high dimensions affecting clustering
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D.
All of the above
Solution
The curse of dimensionality encompasses all these challenges, making clustering in high-dimensional spaces difficult.
Correct Answer:
D
— All of the above
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Q. What does the silhouette score measure in clustering?
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A.
The accuracy of predictions
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B.
The compactness and separation of clusters
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C.
The number of clusters
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D.
The speed of the algorithm
Solution
The silhouette score evaluates how similar an object is to its own cluster compared to other clusters.
Correct Answer:
B
— The compactness and separation of clusters
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Q. What is the main advantage of hierarchical clustering?
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A.
It requires a predefined number of clusters
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B.
It can produce a dendrogram for visualizing clusters
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C.
It is faster than K-Means
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D.
It is less sensitive to noise
Solution
Hierarchical clustering creates a dendrogram, which provides a visual representation of the data's clustering structure.
Correct Answer:
B
— It can produce a dendrogram for visualizing clusters
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Q. What is the main advantage of using Gaussian Mixture Models (GMM) over K-Means?
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A.
GMM can handle non-spherical clusters
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B.
GMM is faster
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C.
GMM requires fewer parameters
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D.
GMM is easier to implement
Solution
GMM can model clusters with different shapes and sizes, unlike K-Means which assumes spherical clusters.
Correct Answer:
A
— GMM can handle non-spherical clusters
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Q. What is the main difference between hard and soft clustering?
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A.
Hard clustering assigns points to one cluster, soft clustering assigns probabilities
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B.
Soft clustering is faster than hard clustering
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C.
Hard clustering can handle noise, soft cannot
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D.
There is no difference
Solution
Hard clustering assigns each data point to a single cluster, while soft clustering assigns probabilities to each cluster.
Correct Answer:
A
— Hard clustering assigns points to one cluster, soft clustering assigns probabilities
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Q. What is the purpose of the elbow method in clustering?
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A.
To determine the optimal number of clusters
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B.
To visualize cluster separation
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C.
To evaluate cluster quality
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D.
To reduce dimensionality
Solution
The elbow method helps identify the optimal number of clusters by plotting the explained variance against the number of clusters.
Correct Answer:
A
— To determine the optimal number of clusters
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Q. Which clustering algorithm is based on density?
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A.
K-Means
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B.
Hierarchical Clustering
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C.
DBSCAN
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D.
Gaussian Mixture Model
Solution
DBSCAN (Density-Based Spatial Clustering of Applications with Noise) identifies clusters based on the density of data points.
Correct Answer:
C
— DBSCAN
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Q. Which clustering algorithm is best suited for non-spherical clusters?
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A.
K-Means
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B.
DBSCAN
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C.
Hierarchical Clustering
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D.
Gaussian Mixture Models
Solution
DBSCAN is effective for identifying clusters of varying shapes and densities, making it suitable for non-spherical clusters.
Correct Answer:
B
— DBSCAN
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Q. Which clustering technique can automatically determine the number of clusters?
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A.
K-Means
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B.
Agglomerative Clustering
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C.
DBSCAN
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D.
Mean Shift
Solution
DBSCAN can automatically determine the number of clusters based on the density of data points.
Correct Answer:
C
— DBSCAN
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Q. Which method can be used to determine the optimal number of clusters in K-Means?
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A.
Elbow Method
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B.
Cross-Validation
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C.
Grid Search
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D.
Random Search
Solution
The Elbow Method helps to determine the optimal number of clusters by plotting the explained variance.
Correct Answer:
A
— Elbow Method
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Q. Which of the following is a limitation of K-Means clustering?
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A.
It can handle large datasets
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B.
It is sensitive to outliers
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C.
It can find non-convex clusters
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D.
It requires no prior knowledge of data
Solution
K-Means is sensitive to outliers, which can skew the results and affect cluster centroids.
Correct Answer:
B
— It is sensitive to outliers
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Q. Which of the following is NOT a common application of clustering?
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A.
Market segmentation
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B.
Anomaly detection
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C.
Image classification
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D.
Document clustering
Solution
Image classification typically involves supervised learning, while clustering is unsupervised.
Correct Answer:
C
— Image classification
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