A fast dynamic programming algorithmic framework to achieve optimal univariate k-means, k-median, and k-segments clustering. Minimizing the sum of respective within-cluster distances, the algorithms guarantee optimality and reproducibility. Their advantage over heuristic clustering algorithms in efficiency and accuracy is increasingly pronounced as the number of clusters k increases. Weighted k-means and unweighted k-segments algorithms can also optimally segment time series and perform peak calling. An auxiliary function generates histograms that are adaptive to patterns in data. This package provides a powerful alternative to heuristic methods for univariate data analysis.

Maintainer: Joe Song <joemsong at cs.nmsu.edu>

Author(s): Joe Song*, Haizhou Wang*

Install package and any missing dependencies by running this line in your R console:

install.packages("Ckmeans.1d.dp")

Depends R (>= 2.10.0)
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Suggests testthat, knitr, rmarkdown
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depends
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imports
gsrc, Tnseq
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FunChisq, xgboost
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Package Ckmeans.1d.dp
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Version 4.2.0
Published 2017-05-30
License LGPL (>= 3)
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NeedsCompilation yes
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Package source Ckmeans.1d.dp_4.2.0.tar.gz