heuristic clustering algorithms in efficiency and accuracy is increasingly
pronounced as the number of clusters k increases. With optional weights,
the algorithm can also analyze 1-D signals for segmentation and peak
calling. An auxiliary function can generate adaptive histograms to make
patterns in data stand out. For univariate data analysis, the package
provides a powerful alternative to heuristic clustering algorithms.

Title: Optimal and Fast Univariate Clustering
Description: A fast dynamic programming algorithm for optimal univariate
pronounced as the number of clusters k increases. With optional weights, the
algorithm can also optimally segment time series and perform peak calling. An
auxiliary function generates histograms that are adaptive to patterns in data.
This package provides a powerful alternative to heuristic methods for

Description: A fast dynamic programming algorithmic framework to
achieve optimal univariate k-means, k-median, and k-segments
clustering. Minimizing the sum of respective within-cluster
distances, the algorithms guarantee optimality and
reproducibility. Their advantage over heuristic clustering
algorithms in efficiency and accuracy is increasingly pronounced
as the number of clusters k increases. Weighted k-means and
unweighted k-segments algorithms can also optimally segment time
series and perform peak calling. An auxiliary function generates
histograms that are adaptive to patterns in data. This package
provides a powerful alternative to heuristic methods for

Package: Ckmeans.1d.dp
Type: Package
Version: 4.2.1
Date: 2017-07-07
Title: Optimal and Fast Univariate Clustering
Authors@R: c(person("Joe", "Song", role = c("aut", "cre"),
email = "joemsong@cs.nmsu.edu"),
person("Haizhou", "Wang", role = "aut"))
Author: Joe Song [aut, cre], Haizhou Wang [aut]
Maintainer: Joe Song <joemsong@cs.nmsu.edu>
Description: Fast optimal univariate clustering and segementation
by dynamic programming. Three types of problem including
univariate k-means, k-median, and k-segments are solved with
guaranteed optimality and reproducibility. The core algorithm
minimizes the sum of within-cluster distances using respective
metrics. Its advantage over heuristic clustering algorithms in
efficiency and accuracy is increasingly pronounced as the
number of clusters k increases. Weighted k-means and unweighted
k-segments algorithms can also optimally segment time series
and perform peak calling. An auxiliary function generates
histograms that are adaptive to patterns in data. In contrast to
heuristic methods, this package provides a powerful set of tools
for univariate data analysis with guaranteed optimality.
License: LGPL (>= 3)
NeedsCompilation: yes
Suggests: testthat, knitr, rmarkdown
Depends: R (>= 2.10.0)
LazyData: true
VignetteBuilder: knitr
Packaged: 2017-07-08 06:04:27 UTC; joemsong
Repository: CRAN
Date/Publication: 2017-07-09 06:24:02 UTC