[jira] [Created] (SPARK-21244) KMeans applied to processed text day clumps almost all documents into one cluster

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

Nassir created SPARK-21244:
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             Summary: 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)



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