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Related Questions
- What are the key differences between Manhattan distance and Euclidean distance in clustering?
- Can you provide examples of real-world applications where Manhattan distance is more suitable than Euclidean distance?
- In what scenarios would a researcher prefer to use Manhattan distance over Euclidean distance in clustering algorithms?
- How does the choice of distance metric impact the clustering results in real-world applications?
- Can you explain the advantages of using Manhattan distance in clustering for high-dimensional data?
- What are some common use cases where Manhattan distance is preferred over Euclidean distance in k-means clustering?
- How does the Manhattan distance metric compare to Euclidean distance in terms of computational complexity and clustering accuracy?
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