arc: Association Rule Classification

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Implements the Classification-based on Association Rules (CBA) algorithm for association rule classification (ARC). The package also 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.

Author
Tomas Kliegr [aut, cre]
Date of publication
2016-09-07 18:22:54
Maintainer
Tomas Kliegr <kliegr@gmail.com>
License
AGPL-3
Version
1.0

View on CRAN

Man pages

applyCut
Apply Cut Points to Vector
applyCuts
Apply Cut Points to Data Frame
cba
CBA Classifier
cbaCSV
Example CBA Workflow with CSV Input
cbaIris
Test CBA Workflow on Iris Dataset
discretizeUnsupervised
Unsupervised Discretization
discrNumeric
Discretize Numeric Columns In Data frame
getAppearance
Method that generates items for values in given data frame...
mdlp2
Supervised Discretization
predict.RuleModel
Apply Rule Model
prune
Classifier Builder
rulemodelAccuracy
Prediction Accuracy
RuleModel-class
RuleModel
topRules
Rule Generation

Files in this package

arc
arc/tests
arc/tests/testthat
arc/tests/testthat/testiris.R
arc/tests/testthat/testthat.R
arc/NAMESPACE
arc/R
arc/R/toprules.R
arc/R/cba.R
arc/R/cutSemicolon.R
arc/R/m1prune.R
arc/R/mdlp2.R
arc/README.md
arc/MD5
arc/DESCRIPTION
arc/man
arc/man/cba.Rd
arc/man/discretizeUnsupervised.Rd
arc/man/cbaIris.Rd
arc/man/discrNumeric.Rd
arc/man/getAppearance.Rd
arc/man/prune.Rd
arc/man/RuleModel-class.Rd
arc/man/predict.RuleModel.Rd
arc/man/applyCuts.Rd
arc/man/applyCut.Rd
arc/man/mdlp2.Rd
arc/man/cbaCSV.Rd
arc/man/rulemodelAccuracy.Rd
arc/man/topRules.Rd