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Posted to issues@commons.apache.org by "ASF GitHub Bot (Jira)" <ji...@apache.org> on 2020/03/22 04:10:00 UTC
[jira] [Work logged] (MATH-1509) Implement the
MiniBatchKMeansClusterer
[ https://issues.apache.org/jira/browse/MATH-1509?focusedWorklogId=407535&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-407535 ]
ASF GitHub Bot logged work on MATH-1509:
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Author: ASF GitHub Bot
Created on: 22/Mar/20 04:09
Start Date: 22/Mar/20 04:09
Worklog Time Spent: 10m
Work Description: chentao106 commented on pull request #128: #MATH-1509: Implement the MiniBatchKMeansClusterer.
URL: https://github.com/apache/commons-math/pull/128
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Issue Time Tracking
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Worklog Id: (was: 407535)
Remaining Estimate: 0h
Time Spent: 10m
> 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: 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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