subgroup.discovery
0.3.1Subgroup Discovery and Bump Hunting
Overview
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.
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5 releases. Pick two to compare their code metrics. R releases are shown for context.
- RR 4.0.0 released · 2020-04-24
- archivedRemoved from CRAN2020-03-15check problems were not corrected in time UBSAN issues, Solaris test failure (timeout, possibly segfault)
- 0.3.12020-02-09 · diff ↗
- 0.3.02020-02-03 · diff ↗
- 0.2.12019-06-21 · diff ↗
- RR 3.6.0 released · 2019-04-26
- RR 3.5.0 released · 2018-04-23
- 0.2.02017-08-02 · diff ↗
- 0.1.32017-07-20
- RR 3.4.0 released · 2017-04-21
Package metadata
- Total releases
- 5
- License
- GPL-3 OSI
- Minimum R
- ≥ 3.6.0
- Bundled data
- 115 KB / 3 files
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