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Related Questions
- What are the advantages and disadvantages of using Euclidean distance in clustering?
- How does Manhattan distance differ from Euclidean distance in terms of data distribution and dimensionality?
- Can you explain the concept of k-means clustering with both Euclidean and Manhattan distance metrics?
- What are some real-world applications where Manhattan distance is preferred over Euclidean distance in clustering?
- How does the choice of distance metric impact the interpretability of clustering results?
- Can you compare the computational complexity of Euclidean and Manhattan distance calculations in clustering?
- How does the choice of distance metric affect the stability of clustering results in the presence of outliers?
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