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Related Questions
- Can the Silhouette score be used as a validity index for clustered data sets with varying geometrical features?
- Is the Silhouette coefficient more robust in detecting elongated and spherical clusters due to its shape independence feature?
- Can clustering algorithms such as k-means, hierarchical clustering and DBSCAN produce Silhouette scores, indicating their strengths and limitations?
- Does the Silhouette index allow us to assess and evaluate clustering algorithms that assume isotropy or homoscedasticity in our data sets?
- Is there any known impact of different dimensionality features (compact or non-linear features) on the resulting Silhouette score of datasets with overlapping clusters?
- Do any known strategies allow using the Silhouette index effectively when data-points fall far apart but adhere to their cluster assignments tightly within,
- Does high or low degree of spatial heterogeneity as compared with compact and nearly perfectly isotropic shapes show clear divergence from typical usage expectations as reflected by significant decrease Silhouette value ranges of up to or as large than what the users is asking to explore what Silhouette has its bounds or else where its better placed?
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