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https://issues.apache.org/jira/browse/SPARK-21244?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Sean Owen resolved SPARK-21244.
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Resolution: Not A Problem
> KMeans applied to processed text day clumps almost all documents into one
> cluster
> ---------------------------------------------------------------------------------
>
> Key: SPARK-21244
> URL: https://issues.apache.org/jira/browse/SPARK-21244
> Project: Spark
> Issue Type: Bug
> Components: ML
> Affects Versions: 2.1.1
> Reporter: Nassir
>
> I have observed this problem for quite a while now regarding the
> implementation of pyspark KMeans on text documents - to cluster documents
> according to their TF-IDF vectors. The pyspark implementation - even on
> standard datasets - clusters almost all of the documents into one cluster.
> I implemented K-means on the same dataset with same parameters using SKlearn
> library, and this clusters the documents very well.
> I recommend anyone who is able to test the pyspark implementation of KMeans
> on text documents - which obviously has a bug in it somewhere.
> (currently I am convert my spark dataframe to pandas dataframe and running k
> means and converting back. However, this is of course not a parallel solution
> capable of handling huge amounts of data in future)
> Here is a link to the question i posted a while back on stackoverlfow:
> https://stackoverflow.com/questions/43863373/tf-idf-document-clustering-with-k-means-in-apache-spark-putting-points-into-one
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