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 discretization classifier <- CBA(Species ~ ., data = iris, supp = 0.05, conf = 0.9) classifier CBA Classifier Object Class: Species=setosa, Species=versicolor, Species=virginica Default Class: Species=setosa Number of rules: 8 Classification method: first Description: CBA algorithm by Liu, et al. 1998 with support=0.05 and confidence=0.9 # make predictions for the first few instances of iris predict(classifier, head(iris))  setosa setosa setosa setosa setosa setosa Levels: setosa versicolor virginica