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Related Questions
- How does the choice of distance metric affect the stability of k-means clustering results when dealing with high-dimensional data?
- Can you explain the relationship between the Euclidean distance and the robustness of k-means clustering to outliers?
- How does the Mahalanobis distance compare to other distance metrics in terms of its ability to handle noisy or outlier-prone data in k-means clustering?
- What is the impact of using the Manhattan distance on the robustness of k-means clustering in the presence of outliers?
- Can you discuss the trade-off between using a sensitive distance metric like the Euclidean distance and a more robust metric like the Mahalanobis distance in k-means clustering?
- How does the choice of distance measure influence the sensitivity of k-means clustering to noisy or anomalous data points?
- What are the implications of using a distance metric that is not scale-invariant on the robustness of k-means clustering to outliers?
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