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Related Questions
- What are the common distance metrics used in clustering algorithms and how do they impact cluster compactness and separation?
- Can you explain the effect of using Euclidean distance versus Manhattan distance on clustering results in terms of cluster compactness and separation?
- How does the choice of distance metric influence the quality of clusters in terms of within-cluster similarity and between-cluster dissimilarity?
- What are the implications of using a distance metric with a high bias on clustering results, particularly in terms of cluster compactness and separation?
- How does the choice of distance metric affect the stability of clustering results when dealing with noisy or high-dimensional data?
- Can you compare and contrast the effects of using different distance metrics, such as cosine similarity versus Euclidean distance, on cluster compactness and separation?
- What are the theoretical foundations behind the choice of distance metric in clustering, and how do they impact cluster compactness and separation in practice?
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