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https://issues.apache.org/jira/browse/SPARK-6137?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14345484#comment-14345484
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Denis Dus commented on SPARK-6137:
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Streaming K-Means (as I see) is just a variation of regular k-means, whish is
designed specially for streaming data. So, you still should define K while
buiding StreamingKMeans model.
GMeans is another algorithm, that uses regular k-means internally. While using
GMeans you don't need to specify K parameter (it will be chosen automatically
during learning process). So, you can think about it as an additional
statistical wrapping for k-means.
As I understand, this algorithms are not related at all. They even have
different spheres of usage (GMeans is for batch data, while streaming k-means
is for streaming data).
> G-Means clustering algorithm implementation
> -------------------------------------------
>
> Key: SPARK-6137
> URL: https://issues.apache.org/jira/browse/SPARK-6137
> Project: Spark
> Issue Type: New Feature
> Components: MLlib
> Reporter: Denis Dus
> Priority: Minor
>
> Will it be useful to implement G-Means clustering algorithm based on K-Means?
> G-means is a powerful extension of k-means, which uses test of cluster data
> normality to decide if it necessary to split current cluster into new two.
> It's relative complexity (compared to k-Means) is O(K), where K is maximum
> number of clusters.
> The original paper is by Greg Hamerly and Charles Elkan from University of
> California:
> [http://papers.nips.cc/paper/2526-learning-the-k-in-k-means.pdf]
> I also have a small prototype of this algorithm written in R (if anyone is
> interested in it).
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