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https://issues.apache.org/jira/browse/MATH-1509?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Chen Tao updated MATH-1509:
---------------------------
Description:
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 if my implemention compare with KMeansPlusPlusClusterer
I have created a pull request on
[https://github.com/apache/commons-math/pull/117], for reference only.
was:
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 if my implemention compare with KMeansPlusPlusClusterer
!image-2020-01-17-11-22-32-434.png!
I have created a pull request on
[https://github.com/apache/commons-math/pull/117], for reference only.
> 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 if my implemention compare with KMeansPlusPlusClusterer
>
>
> I have created a pull request on
> [https://github.com/apache/commons-math/pull/117], for reference only.
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