doi:10.32614/RJ-2019-048>, contains several convenience methods that allow to automatically set CBA parameters (minimum confidence, minimum support) and it also natively handles numeric attributes by integrating a pre-discretization step. The rule generation phase is handled by the 'arules' package. To further decrease the size of the CBA models produced by the 'arc' package, postprocessing by the 'qCBA' package is suggested.">

arc: Association Rule Classification (original) (raw)

Implements the Classification-based on Association Rules (CBA) algorithm for association rule classification. The package, also described in Hahsler et al. (2019) <doi:10.32614/RJ-2019-048>, contains several convenience methods that allow to automatically set CBA parameters (minimum confidence, minimum support) and it also natively handles numeric attributes by integrating a pre-discretization step. The rule generation phase is handled by the 'arules' package. To further decrease the size of the CBA models produced by the 'arc' package, postprocessing by the 'qCBA' package is suggested.

Version: 1.4.2
Depends: R (≥ 3.5.0), arules (≥ 1.7-4), R.utils, discretization
Imports: Matrix (≥ 0.5-0), methods, datasets, utils
Suggests: qCBA
Published: 2025-04-03
DOI: 10.32614/CRAN.package.arc
Author: Tomas Kliegr [aut, cre]
Maintainer: Tomas Kliegr
BugReports: https://github.com/kliegr/arc/issues
License: GPL-3
Copyright: The mdlp2.R script reuses parts of the code from the R `discretization` package by HyunJi Kim (GPL license).
URL: https://github.com/kliegr/arc
NeedsCompilation: no
Materials:
CRAN checks: arc results

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