Q. In which real-world application is K-means clustering often used?
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A.
Spam detection in emails
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B.
Customer segmentation in marketing
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C.
Image recognition
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D.
Natural language processing
Solution
K-means clustering is often used for customer segmentation in marketing to identify distinct customer groups.
Correct Answer:
B
— Customer segmentation in marketing
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Q. What is a common application of K-means clustering in marketing?
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A.
Predicting customer behavior
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B.
Segmenting customers into distinct groups
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C.
Optimizing supply chain logistics
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D.
Analyzing financial trends
Solution
K-means clustering is commonly used to segment customers into distinct groups for targeted marketing.
Correct Answer:
B
— Segmenting customers into distinct groups
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Q. What is a limitation of K-means clustering?
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A.
It can only handle numerical data
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B.
It requires the number of clusters to be specified in advance
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C.
It is sensitive to outliers
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D.
All of the above
Solution
K-means clustering has several limitations, including the need to specify the number of clusters and sensitivity to outliers.
Correct Answer:
D
— All of the above
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Q. What type of data is best suited for hierarchical clustering?
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A.
Large datasets with millions of points
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B.
Data with a clear number of clusters
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C.
Data where relationships between clusters are important
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D.
Data that is linearly separable
Solution
Hierarchical clustering is best suited for data where relationships between clusters are important.
Correct Answer:
C
— Data where relationships between clusters are important
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Q. Which clustering method can automatically determine the number of clusters?
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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 Models
Solution
DBSCAN can automatically determine the number of clusters based on the density of data points.
Correct Answer:
C
— DBSCAN
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