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https://issues.apache.org/jira/browse/SPARK-3220?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15140623#comment-15140623
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Tsai Li Ming commented on SPARK-3220:
-------------------------------------
I built Derrick's kmeans against Spark 1.6.0 and ran
{code}
import com.massivedatascience.clusterer.KMeans
val clusters = KMeans.train(parsedData, numClusters, numIterations)
{code}
It took 41mins with the same dataset/settings compared to 1hr using Mllib. In
both cases, there was enough memory to cache everything.
> K-Means clusterer should perform K-Means initialization in parallel
> -------------------------------------------------------------------
>
> Key: SPARK-3220
> URL: https://issues.apache.org/jira/browse/SPARK-3220
> Project: Spark
> Issue Type: Improvement
> Components: MLlib
> Reporter: Derrick Burns
> Labels: clustering
>
> The LocalKMeans method should be replaced with a parallel implementation. As
> it stands now, it becomes a bottleneck for large data sets.
> I have implemented this functionality in my version of the clusterer.
> However, I see that there are hundreds of outstanding pull requests. If
> someone on the team wants to sponsor the pull request, I will create one.
> Otherwise, I will just maintain my own private fork of the clusterer.
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