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 |

Reference manual: | skm.pdf |

Vignettes: |
skm: selective k-means. |

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 |

OS X El Capitan binaries: | r-release: skm_0.1.5.4.tgz |

OS X Mavericks binaries: | r-oldrel: skm_0.1.5.4.tgz |

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