[PR] [MLLIB] Exclude zero-weight observations from KMeans initialization [spark]

["Israelobuk (via GitHub)" <gi...@apache.org>]

Israelobuk opened a new pull request, #57857:
URL: https://github.com/apache/spark/pull/57857

   ## What changes were proposed in this pull request?
   
   This change prevents zero-weight observations from influencing KMeans centroid initialization.
   
   The row solver now excludes zero-weight observations before RANDOM or k-means|| initialization. The block solver now carries `weightCol` into initialization and excludes zero-weight observations there as well.
   
   ## Why are the changes needed?
   
   Weighted KMeans ignores zero-weight observations during cluster updates, but initialization could still select them as candidate centers. This meant adding a zero-weight outlier could change the fitted model.
   
   ## Does this PR introduce any user-facing changes?
   
   It makes initialization consistent with the existing weighted update semantics: zero-weight observations no longer affect the fitted clusters.
   
   ## How was this patch tested?
   
   A regression test compares datasets with and without an extreme zero-weight outlier across ROW and BLOCK solvers and RANDOM and k-means|| initialization.
   
   `git diff --check` passes. The focused Spark suite could not run in the available Windows environment because WSL `/bin/bash` is unavailable.
   


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Re: [PR] [MINOR][MLLIB] Exclude zero-weight observations from KMeans initialization [spark]

["Israelobuk (via GitHub)" <gi...@apache.org>]

Israelobuk closed pull request #57857: [MINOR][MLLIB] Exclude zero-weight observations from KMeans initialization
URL: https://github.com/apache/spark/pull/57857


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