Developed to assist in discovering interesting subgroups in high-dimensional data. The PRIM implementation is based on the 1998 paper "Bump hunting in high-dimensional data" by Jerome H. Friedman and Nicholas I. Fisher <doi:10.1023/A:1008894516817>. PRIM involves finding a set of "rules" which combined imply unusually large values of some other target variable. Specifically one tries to find a set of sub regions in which the target variable is substantially larger than overall mean. The objective of bump hunting in general is to find regions in the input (attribute/feature) space with relatively high values for the target variable. The regions are described by simple rules of the type if: condition-1 and ... and condition-n then: estimated target value. Given the data (or a subset of the data), the goal is to produce a box B within which the target mean is as large as possible.

Version: | 0.3.1 |

Depends: | R (≥ 3.6.0) |

Imports: | Rcpp (≥ 1.0.3), RcppParallel (≥ 4.4.4) |

LinkingTo: | Rcpp, RcppParallel, BH |

Suggests: | testthat (≥ 2.1.1) |

Published: | 2020-02-09 |

Author: | Jurian Baas [aut, cre, cph], Ad Feelders [ctb] |

Maintainer: | Jurian Baas <j.baas at uu.nl> |

BugReports: | https://github.com/Jurian/subgroup.discovery/issues |

License: | GPL-3 |

URL: | https://github.com/Jurian/subgroup.discovery |

NeedsCompilation: | yes |

SystemRequirements: | GNU make, C++11 |

Language: | en-US |

Materials: | README NEWS |

CRAN checks: | subgroup.discovery results |

Reference manual: | subgroup.discovery.pdf |

Package source: | subgroup.discovery_0.3.1.tar.gz |

Windows binaries: | r-devel: subgroup.discovery_0.3.1.zip, r-devel-gcc8: subgroup.discovery_0.3.1.zip, r-release: subgroup.discovery_0.3.1.zip, r-oldrel: subgroup.discovery_0.2.1.zip |

OS X binaries: | r-release: subgroup.discovery_0.3.1.tgz, r-oldrel: subgroup.discovery_0.2.1.tgz |

Old sources: | subgroup.discovery archive |

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