Provides a function to build an association rule-based classifier for data frames, and to classify incoming data frames using such a classifier.
This R package is an extension of the package arules to perform association rule-based classification. It includes currently two classification algorithms. The first is the CBA algorithm described in Liu, et al. 1998. The second is a new weighted majority-vote based algorithm called bCBA which is currently being designed and tested. Time-critical sections of the code are implemented in C.
The package also provides support for supervised discretization and mining Class Association Rules (CARs).
Stable CRAN version: install from within R with
Current development version:
library("arulesCBA")data("iris")# learn a classifier using automatic default discretizationclassifier <- CBA(Species ~ ., data = iris, supp = 0.05, conf = 0.9)classifierCBA Classifier ObjectClass: Species=setosa, Species=versicolor, Species=virginicaDefault Class: Species=setosaNumber of rules: 8Classification method: firstDescription: CBA algorithm by Liu, et al. 1998 with support=0.05 and confidence=0.9# make predictions for the first few instances of irispredict(classifier, head(iris)) setosa setosa setosa setosa setosa setosaLevels: setosa versicolor virginica