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Related Questions
- What are the key differences between Euclidean and Manhattan distance metrics in clustering algorithms?
- How do Euclidean and Manhattan distances affect the shape and structure of clusters in a dataset?
- Can you provide examples of scenarios where Euclidean distance is preferred over Manhattan distance, and vice versa?
- How do the choice of distance metric and clustering algorithm impact the interpretability of cluster results?
- What are some common applications where Manhattan distance is more suitable than Euclidean distance, and vice versa?
- Can you explain how the choice of distance metric affects the stability and robustness of clustering results?
- How do Euclidean and Manhattan distances influence the computation time and complexity of clustering algorithms?
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