subgroup.discovery: Subgroup Discovery and Bump Hunting

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>
License: GPL-3
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:, r-devel-gcc8:, r-release:, r-oldrel:
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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