Ckmeans.1d.dp: Optimal and Fast Univariate Clustering

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.

Version: 4.2.0
Depends: R (≥ 2.10.0)
Suggests: testthat, knitr, rmarkdown
Published: 2017-05-30
Author: Joe Song [aut, cre], Haizhou Wang [aut]
Maintainer: Joe Song <joemsong at cs.nmsu.edu>
License: LGPL (≥ 3)
NeedsCompilation: yes
Citation: Ckmeans.1d.dp citation info
Materials: NEWS
CRAN checks: Ckmeans.1d.dp results

Downloads:

Reference manual: Ckmeans.1d.dp.pdf
Vignettes: Tutorial: Optimal univariate clustering
Tutorial: Adaptive versus regular histograms
Package source: Ckmeans.1d.dp_4.2.0.tar.gz
Windows binaries: r-devel: Ckmeans.1d.dp_4.2.0.zip, r-release: Ckmeans.1d.dp_4.2.0.zip, r-oldrel: Ckmeans.1d.dp_4.2.0.zip
OS X El Capitan binaries: r-release: Ckmeans.1d.dp_4.2.0.tgz
OS X Mavericks binaries: r-oldrel: Ckmeans.1d.dp_4.2.0.tgz
Old sources: Ckmeans.1d.dp archive

Reverse dependencies:

Reverse imports: gsrc, Tnseq
Reverse suggests: FunChisq, xgboost

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