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Related Questions
- What are the key differences between Euclidean and Manhattan distance in k-means clustering?
- How does Euclidean distance affect the shape of clusters in k-means clustering?
- What are the implications of using Manhattan distance on the cluster centroid initialization in k-means clustering?
- Can you explain the effect of Euclidean distance on the convergence rate of k-means clustering?
- How does the choice of distance metric (Euclidean vs Manhattan) impact the interpretability of k-means clustering results?
- What are the computational costs associated with using Euclidean distance versus Manhattan distance in k-means clustering?
- Can you provide an example scenario where Manhattan distance is more suitable than Euclidean distance in k-means clustering?
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