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Related Questions
- Can different distance metrics be used to identify clusters with varying densities and complex structures?
- How do the characteristics of a distance metric, such as its asymmetry and non-metricity, impact the identification of clusters with different densities and structures?
- Can the choice of distance metric affect the ability to identify clusters with varying density and structure in high-dimensional data?
- How do different distance metrics, such as Euclidean, Manhattan, and Cosine, perform in identifying clusters with different densities and structures?
- Can the choice of distance metric impact the interpretation of cluster boundaries and the identification of outliers in clusters with varying densities and structures?
- How can the use of multiple distance metrics be used to identify clusters with different densities and structures in a single dataset?
- Can the choice of distance metric be used to identify clusters with different structures, such as linear, spherical, or hierarchical structures?
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