Ckmeans.1d.dp: Optimal and Fast Univariate k-Means Clustering

A dynamic programming algorithm for optimal univariate k-means clustering. Minimizing the sum of squares of within-cluster distances, the algorithm guarantees optimality and reproducibility. Its advantage over heuristic k-means algorithms in efficiency and accuracy is increasingly pronounced as the number of clusters k increases. It provides an alternative to heuristic k-means algorithms for univariate clustering.

Version: 3.4.6-4
Depends: R (≥ 2.10.0)
Suggests: testthat
Published: 2016-10-22
Author: Joe Song [aut, cre], Haizhou Wang [aut]
Maintainer: Joe Song <joemsong at>
License: LGPL (≥ 3)
NeedsCompilation: yes
Citation: Ckmeans.1d.dp citation info
Materials: NEWS
CRAN checks: Ckmeans.1d.dp results


Reference manual: Ckmeans.1d.dp.pdf
Package source: Ckmeans.1d.dp_3.4.6-4.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X Mavericks binaries: r-release: Ckmeans.1d.dp_3.4.6-4.tgz, r-oldrel: Ckmeans.1d.dp_3.4.6-4.tgz
Old sources: Ckmeans.1d.dp archive

Reverse dependencies:

Reverse imports: gsrc
Reverse suggests: FunChisq, xgboost


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