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 heuristic k-means algorithms, this method guarantees optimality and reproducibility. Its advantage in efficiency and accuracy over k-means is increasingly pronounced as the number of clusters k increases.

Version: 3.4.0-1
Depends: R (≥ 2.10.0)
Suggests: testthat
Published: 2016-05-19
Author: Joe Song and Haizhou Wang
Maintainer: Joe Song <joemsong at cs.nmsu.edu>
BugReports: NA
License: LGPL (≥ 3)
URL: NA
NeedsCompilation: yes
Citation: Ckmeans.1d.dp citation info
Materials: NEWS
CRAN checks: Ckmeans.1d.dp results

Downloads:

Reference manual: Ckmeans.1d.dp.pdf
Package source: Ckmeans.1d.dp_3.4.0-1.tar.gz
Windows binaries: r-devel: Ckmeans.1d.dp_3.4.0-1.zip, r-release: Ckmeans.1d.dp_3.4.0-1.zip, r-oldrel: Ckmeans.1d.dp_3.4.0-1.zip
OS X Mavericks binaries: r-release: Ckmeans.1d.dp_3.4.0-1.tgz, r-oldrel: Ckmeans.1d.dp_3.4.0-1.tgz
Old sources: Ckmeans.1d.dp archive

Reverse dependencies:

Reverse suggests: FunChisq, xgboost