Nassir created SPARK-21244:
------------------------------
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)
--
This message was sent by Atlassian JIRA
(v6.4.14#64029)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org
For additional commands, e-mail: issues-h...@spark.apache.org
