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.

Getting started

Package details

AuthorJurian Baas [aut, cre, cph], Ad Feelders [ctb]
MaintainerJurian Baas <>
Package repositoryView on CRAN
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subgroup.discovery documentation built on Feb. 4, 2020, 9:11 a.m.