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Q1. Which clustering method is more suitable for discovering non-spherical clusters?
Solution:
Hierarchical clustering can be more suitable for discovering non-spherical clusters as it does not assume a specific shape for the clusters.
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Q2. Which of the following is a characteristic of hierarchical clustering?
Solution:
Hierarchical clustering can produce a dendrogram, which is a tree-like diagram that shows the arrangement of the clusters.
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Q3. Which of the following is NOT a common initialization method for K-means?
Solution:
Hierarchical initialization is not a common method for initializing K-means; the other three are standard techniques.
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Q4. What is a potential drawback of hierarchical clustering?
Solution:
Hierarchical clustering can be computationally expensive, especially for large datasets, due to its complexity.
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Q5. What is a common application of clustering methods in real-world scenarios?
Solution:
Clustering methods are commonly used to segment customers based on purchasing behavior, allowing businesses to tailor marketing strategies.
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Q6. Which clustering method is more sensitive to outliers?
Solution:
K-means clustering is more sensitive to outliers because it uses mean values to determine cluster centroids, which can be skewed by extreme values.
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