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 (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: 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. 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 socalled covering strategy. This means that the same box construction (rule induction) algorithm is applied sequentially to subsets of the data.
Package details 


Maintainer  
License  GPL3 
Version  0.2.0 
URL  https://github.com/Jurian/subgroup.discovery 
Package repository  View on GitHub 
Installation 
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