Q. In a case study using K-Means clustering, what is a common method to determine the optimal number of clusters?
A.
Cross-validation
B.
Elbow method
C.
Grid search
D.
Random search
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Solution
The Elbow method helps identify the optimal number of clusters by plotting the explained variance against the number of clusters.
Correct Answer:
B
— Elbow method
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Q. In a clustering case study, which metric is often used to evaluate the quality of clusters?
A.
Mean Squared Error
B.
Silhouette Score
C.
Accuracy
D.
F1 Score
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Solution
The Silhouette Score is commonly used to evaluate the quality of clusters by measuring how similar an object is to its own cluster compared to other clusters.
Correct Answer:
B
— Silhouette Score
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Q. In a clustering case study, which of the following is a real-world application?
A.
Spam detection in emails
B.
Customer segmentation in marketing
C.
Predicting stock prices
D.
Image classification
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Solution
Customer segmentation in marketing is a common application of clustering to identify distinct customer groups.
Correct Answer:
B
— Customer segmentation in marketing
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Q. What does the term 'centroid' refer to in K-Means clustering?
A.
The point that represents the center of a cluster
B.
The maximum distance between points in a cluster
C.
The average distance of points from the origin
D.
The total number of clusters formed
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Solution
In K-Means clustering, a centroid refers to the point that represents the center of a cluster.
Correct Answer:
A
— The point that represents the center of a cluster
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Q. What is a common application of clustering in market segmentation?
A.
Predicting customer churn
B.
Identifying customer groups with similar behaviors
C.
Forecasting sales trends
D.
Optimizing supply chain logistics
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Solution
Clustering is used in market segmentation to identify customer groups with similar behaviors for targeted marketing.
Correct Answer:
B
— Identifying customer groups with similar behaviors
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Q. What is a potential drawback of K-Means clustering?
A.
It can handle non-linear data well
B.
It requires the number of clusters to be specified in advance
C.
It is computationally inexpensive
D.
It is robust to outliers
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Solution
K-Means requires the number of clusters to be specified beforehand, which can be a limitation.
Correct Answer:
B
— It requires the number of clusters to be specified in advance
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Q. What is the main advantage of using Gaussian Mixture Models (GMM) for clustering?
A.
It is faster than K-Means
B.
It can model clusters with different shapes and sizes
C.
It requires no prior knowledge of the number of clusters
D.
It is less sensitive to outliers
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Solution
The main advantage of using Gaussian Mixture Models (GMM) is that it can model clusters with different shapes and sizes.
Correct Answer:
B
— It can model clusters with different shapes and sizes
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Q. What type of data is typically used in clustering algorithms?
A.
Labeled data
B.
Unlabeled data
C.
Time series data
D.
Sequential data
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Solution
Clustering algorithms operate on unlabeled data, as they seek to find inherent groupings.
Correct Answer:
B
— Unlabeled data
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Q. Which clustering algorithm is particularly effective for large datasets with noise?
A.
Hierarchical clustering
B.
DBSCAN
C.
K-Means
D.
Gaussian Mixture Models
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Solution
DBSCAN is effective for large datasets and can identify clusters of varying shapes while handling noise.
Correct Answer:
B
— DBSCAN
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Q. Which of the following is a method to visualize clustering results?
A.
Confusion matrix
B.
ROC curve
C.
Dendrogram
D.
Precision-recall curve
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Solution
A dendrogram is a tree-like diagram that shows the arrangement of the clusters formed in hierarchical clustering.
Correct Answer:
C
— Dendrogram
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Q. Which of the following is NOT a typical use case for clustering?
A.
Image segmentation
B.
Anomaly detection
C.
Predicting stock prices
D.
Document clustering
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Solution
Predicting stock prices is not a typical use case for clustering, as it is a supervised learning task.
Correct Answer:
C
— Predicting stock prices
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