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

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: 3.4.6-2
Depends: R (≥ 2.10.0)
Suggests: testthat
Published: 2016-09-26
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
Package source: Ckmeans.1d.dp_3.4.6-2.tar.gz
Windows binaries: r-devel: Ckmeans.1d.dp_3.4.6-2.zip, r-release: Ckmeans.1d.dp_3.4.6-2.zip, r-oldrel: Ckmeans.1d.dp_3.4.6-1.zip
OS X Mavericks binaries: r-release: Ckmeans.1d.dp_3.4.6-2.tgz, r-oldrel: Ckmeans.1d.dp_3.4.6-2.tgz
Old sources: Ckmeans.1d.dp archive

Reverse dependencies:

Reverse suggests: FunChisq, gsrc, xgboost

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