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Related Questions
- What are the common distance metrics used in clustering algorithms and how do they affect the stability of the results?
- How does the choice of distance metric impact the clustering algorithm's ability to identify meaningful clusters?
- Can you explain the difference in clustering results obtained using Euclidean distance versus Manhattan distance?
- What are the implications of using a non-metric distance metric, such as cosine similarity, on the stability of clustering results?
- How does the choice of distance metric affect the clustering algorithm's sensitivity to outliers and noisy data?
- What role does the choice of distance metric play in determining the number of clusters in a dataset?
- Can you discuss the trade-offs between different distance metrics in terms of computational efficiency and clustering accuracy?
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