README.md
In arulesCBA: Classification Based on Association Rules

R package arulesCBA - Classification Based on Association Rules

License

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, 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},
}

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/"))

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

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. .

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. .

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. .

Hahsler, Michael, Ian Johnson, Tomáš Kliegr, and Jaroslav Kuchař. 2019. “Associative Classification in R: arc, arulesCBA, and rCBA.” *The R Journal* 11: 254–67. .

Hornik, Kurt, Christian Buchta, and Achim Zeileis. 2009. “Open-Source Machine Learning: R Meets Weka.” *Computational Statistics* 24 (2): 225–32. .

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. .

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. .

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.