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Posted to issues@spark.apache.org by "Nassir (JIRA)" <ji...@apache.org> on 2017/06/28 15:24:00 UTC
[jira] [Updated] (SPARK-21244) KMeans applied to processed text day
clumps almost all documents into one cluster
[ https://issues.apache.org/jira/browse/SPARK-21244?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Nassir updated SPARK-21244:
---------------------------
Description:
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
was:
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)
> 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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