The R Journal: article published in 2019, volume 11:2

Associative Classification in R: arc, arulesCBA, and rCBA PDF download
Michael Hahsler, Ian Johnson, Tomáš Kliegr and Jaroslav Kuchař , The R Journal (2019) 11:2, pages 254-267.

Abstract 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.

Received: 2018-05-29; online 2019-12-27, supplementary material, (1.2 KiB)
CRAN packages: arc, arulesCBA, rCBA, RWeka, arules, arulesViz, qcba, sbrl, RKEEL, discretization, Matrix, qCBA, matrix, rJAVA, mlbench, datasets
CRAN Task Views implied by cited CRAN packages: MachineLearning, ModelDeployment, Econometrics, Multivariate, NaturalLanguageProcessing, NumericalMathematics

CC BY 4.0
This article and supplementary materials are licensed under a Creative Commons Attribution 4.0 International license.

  author = {Michael Hahsler and Ian Johnson and Tomáš Kliegr and
          Jaroslav Kuchař},
  title = {{Associative Classification in R: arc, arulesCBA, and rCBA}},
  year = {2019},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2019-048},
  url = {},
  pages = {254--267},
  volume = {11},
  number = {2}