# subgroup.discovery (0.2.1)

Subgroup Discovery and Bump Hunting.

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

http://cran.r-project.org/web/packages/subgroup.discovery

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. PRIM involves finding a set of "rules" which combined imply unusually large (or small) 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 (low) 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. There are many problems where finding such regions is of considerable practical interest. Often these are problems where a decision maker can in a sense choose or select the values of the input variables so as to optimize the value of the target variable. In bump hunting it is customary to follow a so-called covering strategy. This means that the same box construction (rule induction) algorithm is applied sequentially to subsets of the data.

**Maintainer**:
Jurian Baas

**Author(s)**: Jurian Baas [aut, cre, cph], Ad Feelders [ctb]

**License**: GPL-3

**Uses**: *testthat*

Released about 1 month ago.