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https://issues.apache.org/jira/browse/MATH-1435?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Shubham Jindal updated MATH-1435:
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Description:
cKMeans implementation has been described here
https://cran.r-project.org/web/packages/Ckmeans.1d.dp/index.html and
https://journal.r-project.org/archive/2011-2/RJournal_2011-2_Wang+Song.pdf
The algorithm described here is O(kn^2) where k: number of clusters and n:
number of 1D points. But, there exists an efficient implementation in later
versions of cKMeans which is O(knlogn)
cKMeans is faster than kMeans and also deterministic in nature. It is supposed
to be one of the best clustering algorithms for clustering 1D points
was:
cKMeans implementation has been described here
https://cran.r-project.org/web/packages/Ckmeans.1d.dp/index.html and
https://journal.r-project.org/archive/2011-2/RJournal_2011-2_Wang+Song.pdf
The algorithm described here is O(kn^2) where k: number of clusters and n:
number of 1D points. But, there exists an efficient implementation in later
versions of cKMeans which is O(knlogn)
cKMeans is faster than kMeans and also deterministic in nature. cKMeans is
supposed to be one of the best clustering algorithms for clustering 1D points
> Implement cKMeans as a clustering algorithm
> -------------------------------------------
>
> Key: MATH-1435
> URL: https://issues.apache.org/jira/browse/MATH-1435
> Project: Commons Math
> Issue Type: New Feature
> Reporter: Shubham Jindal
>
> cKMeans implementation has been described here
> https://cran.r-project.org/web/packages/Ckmeans.1d.dp/index.html and
> https://journal.r-project.org/archive/2011-2/RJournal_2011-2_Wang+Song.pdf
> The algorithm described here is O(kn^2) where k: number of clusters and n:
> number of 1D points. But, there exists an efficient implementation in later
> versions of cKMeans which is O(knlogn)
> cKMeans is faster than kMeans and also deterministic in nature. It is
> supposed to be one of the best clustering algorithms for clustering 1D points
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