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arc (version 1.4.2)

Association Rule Classification

Description

Implements the Classification-based on Association Rules (CBA) algorithm for association rule classification. The package, also described in Hahsler et al. (2019) , 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.

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Install

install.packages('arc')

Monthly Downloads

516

Version

1.4.2

License

GPL-3

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Maintainer

Tomas Kliegr

Last Published

April 3rd, 2025

Functions in arc (1.4.2)

topRules

Rule Generation
cbaCSV

Example CBA Workflow with CSV Input
cba_manual

CBA Classifier from provided rules
cba

CBA Classifier
applyCuts

Apply Cut Points to Data Frame
applyCut

Apply Cut Points to Vector
CBARuleModel-class

CBARuleModel
cbaIrisNumeric

Test CBA Workflow on Iris Dataset with numeric target
cbaIris

Test CBA Workflow on Iris Dataset
CBARuleModelAccuracy

Prediction Accuracy
discrNumeric

Discretize Numeric Columns In Data frame
predict.CBARuleModel

Apply Rule Model
mdlp2

Supervised Discretization
discretizeUnsupervised

Unsupervised Discretization
prune

Classifier Builder
getConfVectorForROC

Returns vector with confidences for the positive class (useful for ROC or AUC computation)
humtemp

Comfort level based on temperature and humidity of the environment
getAppearance

Method that generates items for values in given data frame column.