Q. What does the 'K' in K-means represent?
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A.
The number of iterations the algorithm runs
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B.
The number of clusters to form
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C.
The number of features in the dataset
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D.
The distance metric used
Solution
The 'K' in K-means represents the number of clusters that the algorithm will form from the dataset.
Correct Answer:
B
— The number of clusters to form
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Q. What is a common method 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 is commonly used to determine the optimal number of clusters by plotting the explained variance against the number of clusters.
Correct Answer:
A
— Elbow method
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Q. Which of the following scenarios is best suited for K-means clustering?
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A.
Identifying customer segments based on purchasing behavior
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B.
Classifying emails as spam or not spam
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C.
Predicting house prices based on features
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D.
Finding the optimal path in a navigation system
Solution
K-means clustering is well-suited for identifying customer segments based on similarities in purchasing behavior.
Correct Answer:
A
— Identifying customer segments based on purchasing behavior
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