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.

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
DOI: 10.32614/CRAN.package.skm
Author: Guang Yang
Maintainer: Guang Yang <gyang274 at>
License: MIT + file LICENSE
NeedsCompilation: yes
SystemRequirements: GNU make
CRAN checks: skm results


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


Package source: skm_0.1.5.4.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
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


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