skm: Selective k-Means

Algorithms for solving selective k-means problem, which is defined as finding k rows in an m x n matrix such that the sum of each column minimal is minimized. In the scenario when m == n and each cell value in matrix is a valid distance metric, this is equivalent to a k-means problem. The selective k-means extends the k-means problem in the sense that it is possible to have m != n, often the case m < n which implies the search is limited within a small subset of rows. Also, the selective k-means extends the k-means problem in the sense that the instance in row set can be instance not seen in the column set, e.g., select 2 from 3 internet service provider (row) for 5 houses (column) such that minimize the overall cost (cell value) - overall cost is the sum of the column minimal of the selected 2 service provider.

Version:	0.1.5.4
Depends:	R (≥ 3.0.0), magrittr, data.table
Imports:	methods, plyr, Rcpp (≥ 0.12.5), RcppParallel
LinkingTo:	Rcpp, RcppArmadillo, RcppParallel
Suggests:	knitr, rmarkdown
Published:	2017-01-23
Author:	Guang Yang
Maintainer:	Guang Yang <gyang274 at gmail.com>
BugReports:	http://github.com/gyang274/skm/issues
License:	MIT + file LICENSE
URL:	http://github.com/gyang274/skm
NeedsCompilation:	yes
SystemRequirements:	GNU make
CRAN checks:	skm results

Documentation:

Reference manual:	skm.pdf
Vignettes:	skm: selective k-means.

Downloads:

Package source:	skm_0.1.5.4.tar.gz
Windows binaries:	r-devel: skm_0.1.5.4.zip, r-release: skm_0.1.5.4.zip, r-oldrel: skm_0.1.5.4.zip
macOS binaries:	r-release (arm64): skm_0.1.5.4.tgz, r-oldrel (arm64): skm_0.1.5.4.tgz, r-release (x86_64): skm_0.1.5.4.tgz, r-oldrel (x86_64): skm_0.1.5.4.tgz

Linking:

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