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 (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.

Getting started

Package details

AuthorJurian Baas [aut, cre, cph], Ad Feelders [ctb]
MaintainerJurian Baas <[email protected]>
LicenseGPL-3
Version0.2.0
URL https://github.com/Jurian/subgroup.discovery
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("subgroup.discovery")

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subgroup.discovery documentation built on Aug. 2, 2017, 5:01 p.m.