Several methods for creating classifiers based on rules discovered via association rule mining have been proposed in the literature. These classifiers are called associative classifiers and the best-known algorithm is Classification Based on Associations (CBA). Interestingly, only very few implementations are available and, until recently, no implementation was available for R. Now, three packages provide CBA. This paper introduces associative classification, the CBA algorithm, and how it can be used in R. A comparison of the three packages is provided to give the potential user an idea about the advantages of each of the implementations. We also show how the packages are related to the existing infrastructure for association rule mining already available in R.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2019-048.zip
arc, arulesCBA, rCBA, RWeka, arules, arulesViz, qcba, sbrl, RKEEL, discretization, Matrix, qCBA, matrix, rJAVA, mlbench, datasets
MachineLearning, ModelDeployment, Econometrics, Multivariate, NaturalLanguageProcessing, NumericalMathematics
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For attribution, please cite this work as
Hahsler, et al., "Associative Classification in R: arc, arulesCBA, and rCBA", The R Journal, 2019
BibTeX citation
@article{RJ-2019-048,
author = {Hahsler, Michael and Johnson, Ian and Kliegr, Tomáš and Kuchař, Jaroslav},
title = {Associative Classification in R: arc, arulesCBA, and rCBA},
journal = {The R Journal},
year = {2019},
note = {https://doi.org/10.32614/RJ-2019-048},
doi = {10.32614/RJ-2019-048},
volume = {11},
issue = {2},
issn = {2073-4859},
pages = {254-267}
}