rcba's Introduction

rCBA

CBA classifier for R

If you publish research that uses rCBA, please cite:

@inproceedings{Kuchar:2015:EasyMiner,
  author    = {Stanislav Vojir and Vaclav Zeman and Jaroslav Kuchar and Tomas Kliegr},
  title     = {EasyMiner/R Preview: Towards a Web Interface for Association Rule Learning and Classification in R},
  booktitle = {Proceedings of the RuleML 2015 Challenge, the Special Track on Rule-based Recommender Systems for the Web of Data, the Special Industry Track and the RuleML 2015 Doctoral Consortium hosted by the 9th International Web Rule Symposium (RuleML 2015), Berlin, Germany, August 2-5, 2015.},
  year      = {2015}
}

Installation

Prerequisites:

Java 8
R packages - devtools, rJava

R dependencies installation

install.packages(c("devtools","rJava"),dependencies=TRUE, repos="http://cran.us.r-project.org")

Reconfiguration of Java in R

sudo R CMD javareconf

Recompile and reinstall rJava

install.packages('rJava', type='source', dependencies=TRUE, repos="http://cran.us.r-project.org")

rCBA installation

library("devtools")
devtools::install_github("jaroslav-kuchar/rCBA")

Usage

library("arules")
library("rCBA")

train <- read.csv("./train.csv",header=TRUE) # read data

txns <- as(train,"transactions") # convert
rules <- apriori(txns, parameter = list(confidence = 0.1, support= 0.1, minlen=1, maxlen=5)) # rule mining
rules <- subset( rules, subset = rhs %pin% "y=") # filter
rulesFrame <- as(rules,"data.frame") # convert

print(nrow(rulesFrame))
prunedRulesFrame <- pruning(train, rulesFrame, method="m2cba") # m2cba(default)|m1cba|dcbrcba
print(nrow(prunedRulesFrame))