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https://issues.apache.org/jira/browse/MATH-1465?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16576710#comment-16576710
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Gilles commented on MATH-1465:
------------------------------
If you intend to implement this, don't forget to use the [development version
of the code (git "master"
branch)|https://git1-us-west.apache.org/repos/asf?p=commons-math.git;a=tree].
> Increase the initial sampling speed of KMeansPlusPlusClusterer for large k
> --------------------------------------------------------------------------
>
> Key: MATH-1465
> URL: https://issues.apache.org/jira/browse/MATH-1465
> Project: Commons Math
> Issue Type: Improvement
> Affects Versions: 3.6.1
> Reporter: yu zhang
> Priority: Minor
> Labels: performance
> Original Estimate: 1,440h
> Remaining Estimate: 1,440h
>
> As the major difference of KMeans++ to classic Kmeans, an initial distance
> square sampling process is executed, the function is named
> "chooseInitialCenters" in the current implementation. The time complexity is
> O(dkn), where d is the dimension, k is the cluster number and n is the number
> of points.
> In paper "_Ackermann, Lammersen, Märtens, Raupach, Sohler, Swierkot.
> StreamKM++: A Clustering Algorithm for Data Streams. In Proceedings of the
> 12th Workshop on Algorithm Engineering and Experiments (ALENEX 2010)_", the
> authors introduced a data structure named "coreset tree" which can reduce the
> complexity of the initial sampling to O(dn log k). This is useful in the
> scenario when value of k is large, say 1000, then the run speed of the
> algorithm will be much faster.
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