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

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

Version: 3.4.6
Depends: R (≥ 2.10.0)
Suggests: testthat
Published: 2016-06-02
Author: Joe Song and Haizhou Wang
Maintainer: Joe Song <joemsong at>
BugReports: NA
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.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X Mavericks binaries: r-release: Ckmeans.1d.dp_3.4.6.tgz, r-oldrel: Ckmeans.1d.dp_3.4.6.tgz
Old sources: Ckmeans.1d.dp archive

Reverse dependencies:

Reverse suggests: FunChisq, gsrc, xgboost