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Related Questions
- What is the difference between Euclidean and Manhattan distance metrics in k-means clustering?
- Can you provide an example of how k-means clustering works with Euclidean distance?
- How does the choice of distance metric affect the results of k-means clustering?
- What are the advantages and disadvantages of using Manhattan distance in k-means clustering?
- Can k-means clustering be used with high-dimensional data, and if so, how does the choice of distance metric impact it?
- How does the initial placement of centroids affect the results of k-means clustering with Euclidean distance?
- What are some real-world applications of k-means clustering with Euclidean distance?
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