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https://issues.apache.org/jira/browse/MATH-1509?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17017951#comment-17017951
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Gilles Sadowski commented on MATH-1509:
---------------------------------------
{quote}workflow
{quote}
For new features, the starting point would be to describe the proposal on the
"dev" ML.
Once the idea is accepted, a JIRA report is created (this is done already ;))
in order to discuss practical details of the implementations (like improvements
to a PR).
> Implement the MiniBatchKMeansClusterer
> --------------------------------------
>
> Key: MATH-1509
> URL: https://issues.apache.org/jira/browse/MATH-1509
> Project: Commons Math
> Issue Type: New Feature
> Reporter: Chen Tao
> Priority: Major
> Attachments: compare.png
>
>
> MiniBatchKMeans is a fast clustering algorithm,
> which use partial points in initialize cluster centers, and mini batch in
> training iterations.
> It can finish in few seconds on clustering millions of data, and has few
> differences between KMeans.
> I have implemented it by Kotlin in my own project, and I'd like to contribute
> the code to Apache Commons Math, of course in java.
> My implemention is base on Apache Commons Math3, refer to Python
> sklearn.cluster.MiniBatchKMeans
> Thought test I found it works well on intensive data, significant performance
> improvement and return value has few difference to KMeans++, but has many
> difference on sparse data.
>
> Below is the comparation of my implemention and KMeansPlusPlusClusterer
> !compare.png!
>
> I have created a pull request on
> [https://github.com/apache/commons-math/pull/117], for reference only.
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