Developed to assist in discovering interesting subgroups in highdimensional data. The PRIM implementation is based on the 1998 paper "Bump hunting in highdimensional 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: condition1 and ... and conditionn 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.
Package details 


Author  Jurian Baas [aut, cre, cph], Ad Feelders [ctb] 
Maintainer  Jurian Baas <j.baas@uu.nl> 
License  GPL3 
Version  0.3.0 
URL  https://github.com/Jurian/subgroup.discovery 
Package repository  View on CRAN 
Installation 
Install the latest version of this package by entering the following in R:

Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.