R package arulesCBA: Classification Based on Association Rules

The R package arulesCBA (Hahsler et al, 2020) is an extension of the package arules 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 et al, 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 (Quinlan, 1993) via J48 in R/Weka.
• FOIL: First-Order Inductive Learner (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 et al, 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.).

Installation

Stable CRAN version: install from within R with

install.packages("arulesCBA")

Current development version: Install from r-universe.

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: NA
## Classification method: first  
## Description: CBA algorithm (Liu et al., 1998)

Inspect the rulebase.

inspect(rules(classifier), linebreak = TRUE)

##     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

• M. Hahsler, I. Johnson, T. Kliegr and J. Kuchar (2019). Associative Classification in R: arc, arulesCBA, and rCBA. The R Journal 11(2), pp. 254-267.
• M. Azmi, G.C. Runger, and A. Berrado (2019). Interpretable regularized class association rules algorithm for classification in a categorical data space. Information Sciences, Volume 483, May 2019, pp. 313-331.
• W. W. Cohen (1995). Fast effective rule induction. In A. Prieditis and S. Russell (eds.), Proceedings of the 12th International Conference on Machine Learning, pp. 115-123. Morgan Kaufmann. ISBN 1-55860-377-8.
• E. Frank and I. H. Witten (1998). Generating accurate rule sets without global optimization. In J. Shavlik (ed.), Machine Learning: Proceedings of the Fifteenth International Conference, Morgan Kaufmann Publishers: San Francisco, CA.
• W. Li, J. Han and J. Pei (2001). CMAR: accurate and efficient classification based on multiple class-association rules, Proceedings 2001 IEEE International Conference on Data Mining, San Jose, CA, USA, pp. 369-376.
• B. Liu, W. Hsu and Y. Ma (1998). Integrating Classification and Association Rule Mining. KDD’98 Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining, New York, AAAI, pp. 80-86.
• R. Quinlan (1993). C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Mateo, CA.
• X. Yin and J. Han (2003). CPAR: Classification based on Predictive Association Rules, Proceedings of the 2003 SIAM International Conference on Data Minin, pp. 331-235.