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https://issues.apache.org/jira/browse/SPARK-25219?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16591423#comment-16591423
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Marco Gaido commented on SPARK-25219:
-------------------------------------
Hi [~VVasanth], a JIRA like this is very difficult to work on: saying that
something returns a result which is not the expected one is not a great
starting point for taking an action.
It would be great if you could provide a simple reproducer. The reproducer
needs to involve only one thing if possible (in this case KMeans, not involving
other transformation), with a set of parameters to reproduce the problem and
the expected result which is returned with the same parameters by the other
libraries.
If the problem is more clear, I am happy to work on it, but first we need to
understand whether this is indeed an issue and how to reproduce it. Thanks.
> KMeans Clustering - Text Data - Results are incorrect
> -----------------------------------------------------
>
> Key: SPARK-25219
> URL: https://issues.apache.org/jira/browse/SPARK-25219
> Project: Spark
> Issue Type: Bug
> Components: ML
> Affects Versions: 2.3.0
> Reporter: Vasanthkumar Velayudham
> Priority: Major
>
> Hello Everyone,
> I am facing issues with the usage of KMeans Clustering on my text data. When
> I apply clustering on my text data, after performing various transformations
> such as RegexTokenizer, Stopword Processing, HashingTF, IDF, generated
> clusters are not proper and one cluster is found to have lot of data points
> assigned to it.
> I am able to perform clustering with similar kind of processing and with the
> same attributes on the SKLearn KMeans algorithm.
> Upon searching in internet, I observe many have reported the same issue with
> KMeans clustering library of Spark.
> Request your help in fixing this issue.
> Please let me know if you require any additional details.
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