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Related Questions
- What are the key differences between Euclidean and Manhattan distance metrics in clustering?
- How does the choice of distance metric affect the number of clusters in a dataset?
- Can you explain the concept of cosine similarity and its application in clustering?
- How does the choice of distance metric impact the interpretability of clustering results in high-dimensional data?
- What are the implications of using a non-Euclidean distance metric, such as Minkowski distance, on clustering results?
- Can you discuss the trade-offs between using a simple distance metric like Euclidean and a more complex metric like Mahalanobis distance?
- How does the choice of distance metric impact the stability of clustering results across different runs of a clustering algorithm?
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