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Related Questions
- How do different distance measures affect the shape and orientation of clusters in k-means clustering?
- What are the advantages and disadvantages of using Euclidean distance versus Manhattan distance in k-means clustering?
- Can you explain the concept of Minkowski distance and how it relates to k-means clustering?
- How do the choice of distance measure and the value of k affect the stability and interpretability of k-means clustering results?
- What are some common use cases where Euclidean distance is preferred over Manhattan distance in k-means clustering?
- Can you provide examples of datasets where Minkowski distance might be more suitable than Euclidean or Manhattan distance?
- How do the computational costs of different distance measures impact the scalability of k-means clustering algorithms?
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