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Nassir commented on SPARK-21244: -------------------------------- Hi, The pyspark k-means implementation is on the same 20 newsgroup document set that sklearn k-means is run on. pyspark version does not produce any meaningful clsuters, unlike the sklearn k-means (both using euclidean distance as a distance measure). The 'bug' is that pyspark k-means applied to tf-idf documents does not provide expected results. I would be interested to know if anyone has used k-means in spark mllib to cluster a standard document set such as the 20 news group set? Do you get almost all the documents clump into one cluster as I do? > 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 -- 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