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https://issues.apache.org/jira/browse/MATH-1509?focusedWorklogId=409577&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-409577
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ASF GitHub Bot logged work on MATH-1509:
----------------------------------------

                Author: ASF GitHub Bot
            Created on: 25/Mar/20 15:24
            Start Date: 25/Mar/20 15:24
    Worklog Time Spent: 10m 
      Work Description: coveralls commented on issue #132: MATH-1509: Add 
missing documentation to class ImprovementEvaluator
URL: https://github.com/apache/commons-math/pull/132#issuecomment-603903887
 
 
   
   [![Coverage 
Status](https://coveralls.io/builds/29609723/badge)](https://coveralls.io/builds/29609723)
   
   Coverage increased (+0.005%) to 90.553% when pulling 
**01227337f8d6645550a9559bef1a57297feab7b6 on 
chentao106:ImprovementEvaluatorDoc** into 
**6b0395898e9469fda20f011ded8dce3f9d0df907 on apache:master**.
   
 
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Issue Time Tracking
-------------------

    Worklog Id:     (was: 409577)
    Time Spent: 1h 10m  (was: 1h)

> 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, intensive-data-comparsion-badcase.png, 
> intensive-data-comparsion.png, random-data-comparison.png
>
>          Time Spent: 1h 10m
>  Remaining Estimate: 0h
>
> 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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