R package arulesCBA - Classification Based on Association Rules

The R package arulesCBA (Hahsler et al. 2019) is an extension of the package arules (Hahsler, Grün, and Hornik 2005) to perform association rule-based classification. The package provides the infrastructure for class association rules and implements associative classifiers based on the following algorithms:

• CBA: Classification Based on Association Rules (Liu, Hsu, and Ma 1998).
• CMAR: Classification based on Multiple Association Rule (Li, Han, and Pei 2001) via LUCS-KDD Software Library.
• CPAR: Classification based on Predictive Association Rules (Yin and Han 2003) via LUCS-KDD Software Library.
• C4.5: Rules extracted from a C4.5 decision tree (J. Ross Quinlan 1993) via J48 in R/Weka.
• FOIL: First-Order Inductive Learner (J. R. Quinlan and Cameron-Jones 1993; Yin and Han 2003).
• PART: Rules from Partial Decision Trees (Frank and Witten 1998) via R/Weka.
• PRM: Predictive Rule Mining (Yin and Han 2003) via LUCS-KDD Software Library.
• RCAR: Regularized Class Association Rules using Logistic Regression (Azmi, Runger, and Berrado 2019).
• RIPPER: Repeated Incremental Pruning to Produce Error Reduction (Cohen 1995) via R/Weka.

The package also provides the infrastructure for associative classification (supervised discetization, mining class association rules (CARs)), and implements various association rule-based classification strategies (first match, majority voting, weighted voting, etc.). Some algorithms are interfaced by the R package R/Weka (Hornik, Buchta, and Zeileis 2009) and the LUCS-KDD Software Library (Coenen 2013).

The following R packages use arulesCBA: arules, qCBA, tidybins

To cite package ‘arulesCBA’ in publications use:

Hahsler M, Johnson I, Kliegr T, Kuchař J (2019). “Associative Classification in R: arc, arulesCBA, and rCBA.” The R Journal, 11, 254-267. ISSN 2073-4859, doi:10.32614/RJ-2019-048 https://doi.org/10.32614/RJ-2019-048.

@Article{,
  title = {{Associative Classification in R: arc, arulesCBA, and rCBA}},
  author = {Michael Hahsler and Ian Johnson and Tom{\'{a}}\v{s} Kliegr and Jaroslav Kucha\v{r}},
  year = {2019},
  journal = {{The R Journal}},
  volume = {11},
  issue = {2},
  pages = {254-267},
  issn = {2073-4859},
  doi = {10.32614/RJ-2019-048},
}

Installation

Stable CRAN version: Install from within R with

install.packages("arulesCBA")

Current development version: Install from r-universe.

install.packages("arulesCBA",
    repos = c("https://mhahsler.r-universe.dev",
              "https://cloud.r-project.org/"))

Usage

library("arulesCBA")
data("iris")

Learn a classifier.

classifier <- CBA(Species ~ ., data = iris)
classifier

## CBA Classifier Object
## Formula: Species ~ .
## Number of rules: 6
## Default Class: versicolor
## Classification method: first  
## Description: CBA algorithm (Liu et al., 1998)

Inspect the rulebase.

inspect(classifier$rules, linebreak = TRUE)

## Warning in seq.default(length = NCOL(quality)): partial argument match of
## 'length' to 'length.out'

##     lhs                            rhs                  support confidence coverage lift count size coveredTransactions totalErrors
## [1] {Petal.Length=[-Inf,2.45)}  => {Species=setosa}        0.33       1.00     0.33  3.0    50    2                  50          50
## [2] {Sepal.Length=[6.15, Inf],                                                                                                     
##      Petal.Width=[1.75, Inf]}   => {Species=virginica}     0.25       1.00     0.25  3.0    37    3                  37          13
## [3] {Sepal.Length=[5.55,6.15),                                                                                                     
##      Petal.Length=[2.45,4.75)}  => {Species=versicolor}    0.14       1.00     0.14  3.0    21    3                  21          13
## [4] {Sepal.Width=[-Inf,2.95),                                                                                                      
##      Petal.Width=[1.75, Inf]}   => {Species=virginica}     0.11       1.00     0.11  3.0    17    3                   5           8
## [5] {Petal.Width=[1.75, Inf]}   => {Species=virginica}     0.30       0.98     0.31  2.9    45    2                   4           6
## [6] {}                          => {Species=versicolor}    0.33       0.33     1.00  1.0   150    1                  33           6

Make predictions for the first few instances of iris.

predict(classifier, head(iris))

## [1] setosa setosa setosa setosa setosa setosa
## Levels: setosa versicolor virginica

References

Azmi, Mohamed, George C. Runger, and Abdelaziz Berrado. 2019. “Interpretable Regularized Class Association Rules Algorithm for Classification in a Categorical Data Space.” Information Sciences 483: 313–31. https://doi.org/10.1016/j.ins.2019.01.047.

Coenen, Frans. 2013. The LUCS-KDD Software Library. University of Liverpool.

Cohen, William W. 1995. “Fast Effective Rule Induction.” In Machine Learning Proceedings 1995, Proceedings of the Twelfth International Conference on Machine Learning, 115–23. Elsevier. https://doi.org/10.1016/B978-1-55860-377-6.50023-2.

Frank, Eibe, and Ian H. Witten. 1998. “Generating Accurate Rule Sets Without Global Optimization.” In Proceedings of the Fifteenth International Conference on Machine Learning, 144–51. ICML ’98. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc.

Hahsler, Michael, Bettina Grün, and Kurt Hornik. 2005. “Arules - a Computational Environment for Mining Association Rules and Frequent Item Sets.” Journal of Statistical Software 14 (15): 1–25. https://doi.org/10.18637/jss.v014.i15.

Hahsler, Michael, Ian Johnson, Tomáš Kliegr, and Jaroslav Kuchař. 2019. “Associative Classification in R: arc, arulesCBA, and rCBA.” The R Journal 11: 254–67. https://doi.org/10.32614/RJ-2019-048.

Hornik, Kurt, Christian Buchta, and Achim Zeileis. 2009. “Open-Source Machine Learning: R Meets Weka.” Computational Statistics 24 (2): 225–32. https://doi.org/10.1007/s00180-008-0119-7.

Li, Wenmin, Jiawei Han, and Jian Pei. 2001. “CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules.” In Proceedings of the 2001 IEEE International Conference on Data Mining, 369–76. ICDM ’01. Washington, DC, USA: IEEE Computer Society. https://doi.org/10.1109/ICDM.2001.989541.

Liu, Bing, Wynne Hsu, and Yiming Ma. 1998. “Integrating Classification and Association Rule Mining.” In Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining, 80–86. KDD’98. AAAI Press.

Quinlan, J Ross. 1993. “C4.5: Programs for Machine Learning.” Morgan Kaufmann Publishers.

Quinlan, J. R., and R. M. Cameron-Jones. 1993. “FOIL: A Midterm Report.” In Proceedings of the 6th European Conference on Machine Learning, 1–20. ECML’93. Berlin, Heidelberg: Springer-Verlag. https://doi.org/10.1007/3-540-56602-3_124.

Yin, Xiaoxin, and Jiawei Han. 2003. “CPAR: Classification Based on Predictive Association Rules.” In Proceedings of the SIAM International Conference on Data Mining, 369–76. San Franciso: SIAM Press.