[jira] [Updated] (SPARK-3218) K-Means clusterer can fail on degenerate data

["Xiangrui Meng (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-3218?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Xiangrui Meng updated SPARK-3218:
---------------------------------
    Target Version/s: 1.3.0  (was: 1.2.0)

> K-Means clusterer can fail on degenerate data
> ---------------------------------------------
>
>                 Key: SPARK-3218
>                 URL: https://issues.apache.org/jira/browse/SPARK-3218
>             Project: Spark
>          Issue Type: Bug
>          Components: MLlib
>    Affects Versions: 1.0.2
>            Reporter: Derrick Burns
>            Assignee: Derrick Burns
>
> The KMeans parallel implementation selects points to be cluster centers with probability weighted by their distance to cluster centers.  However, if there are fewer than k DISTINCT points in the data set, this approach will fail.  
> Further, the recent checkin to work around this problem results in selection of the same point repeatedly as a cluster center. 
> The fix is to allow fewer than k cluster centers to be selected.  This requires several changes to the code, as the number of cluster centers is woven into the implementation.
> I have a version of the code that addresses this problem, AND generalizes the distance metric.  However, I see that there are literally hundreds of outstanding pull requests.  If someone will commit to working with me to sponsor the pull request, I will create it.



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