Ckmeans.1d.dp: Optimal k-Means Clustering for One-Dimensional Data

A dynamic programming algorithm for optimal one-dimensional k-means clustering. The algorithm minimizes the sum of squares of within-cluster distances. As an alternative to the standard heuristic k-means algorithm, this algorithm guarantees optimality and repeatability.

Version: 3.3.1
Depends: R (≥ 2.10.0)
Suggests: testthat
Published: 2015-02-11
Author: Joe Song and Haizhou Wang
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_3.3.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X Snow Leopard binaries: r-release: Ckmeans.1d.dp_3.3.1.tgz, r-oldrel: Ckmeans.1d.dp_3.3.1.tgz
OS X Mavericks binaries: r-release: Ckmeans.1d.dp_3.3.1.tgz
Old sources: Ckmeans.1d.dp archive

Reverse dependencies:

Reverse suggests: FunChisq, xgboost