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Related Questions
- What is the impact of using different distance metrics on the number of clusters in a k-means clustering algorithm?
- Can you provide examples of how the choice of distance metric affects the number of clusters in real-world clustering applications, such as image segmentation or customer segmentation?
- How does the choice of distance metric influence the number of clusters in a hierarchical clustering algorithm?
- What are the differences in cluster formation when using Euclidean distance versus Manhattan distance in a clustering algorithm?
- Can you explain how the choice of distance metric affects the number of clusters in a density-based clustering algorithm, such as DBSCAN?
- How does the choice of distance metric impact the stability of clustering results in the presence of noise or outliers?
- What are the implications of using different distance metrics on the interpretability of clustering results in a real-world application?
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