Added DBScan clustering to ColorPaletteSampler

[Avid29]

The DBScan step runs after KMeans to merge clusters that are too similar.

KMeans cannot be refined on its own to not have this issue, since it simply clusters to K points. Determining the right number for K is no better than just merging the results with another clustering method.

[Copilot]

Pull Request Overview

Enhances the ColorPaletteSampler by integrating DBScan clustering to merge similar KMeans results, addressing the limitation where KMeans clusters to K points regardless of color similarity.

• Added DBScan clustering post-processing to merge KMeans clusters that are too similar
• Introduced a configurable merge distance parameter for similarity threshold
• Modified weight calculation to properly handle cluster merging

Reviewed Changes

Copilot reviewed 3 out of 3 changed files in this pull request and generated 4 comments.

File	Description
ColorPaletteSampler.cs	Added DBScan post-processing step with merge distance parameter and updated weight handling
ColorPaletteSampler.KMeans.cs	Changed method visibility from private to internal to support DBScan integration
ColorPaletteSampler.DBScan.cs	Implemented complete DBScan clustering algorithm as a ref struct with weighted centroid calculation

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