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 implements the following algorithms:

• CBA (Liu et al, 1998)
• bCBA, wCBA (Ian Johnson, unpublished)
• CMAR via LUCS-KDD Software Library (Li, Han and Pei, 2001)
• CPAR via LUCS-KDD Software Library (Yin and Han, 2003)
• C4.5 via J48 in R/Weka (Quinlan, 1993)
• FOIL (Yin and Han, 2003)
• PART via R/Weka (Frank and Witten, 1998)
• PRM via LUCS-KDD Software Library (Yin and Han, 2003)
• RCAR (Azmi et al, 2019)
• RIPPER via R/Weka (Cohen, 1995)

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:

library("devtools")
install_github("ianjjohnson/arulesCBA")

Usage

library("arulesCBA")
data("iris")
 
# learn a classifier
classifier <- CBA(Species ~ ., data = iris)
classifier

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

# inspect the rulebase
inspect(rules(classifier), linebreak = TRUE)
     lhs                           rhs                  support conf lift count 
 [1] {Petal.Length=[-Inf,2.45)} => {Species=setosa}        0.33 1.00  3.0    50 
 [2] {Sepal.Length=[6.15, Inf],       
      Petal.Width=[1.75, Inf]}  => {Species=virginica}     0.25 1.00  3.0    37 
 [3] {Sepal.Length=[5.55,6.15),   
      Petal.Length=[2.45,4.75)} => {Species=versicolor}    0.14 1.00  3.0    21 
 [4] {Sepal.Width=[-Inf,2.95),
      Petal.Width=[1.75, Inf]}  => {Species=virginica}     0.11 1.00  3.0    17
 [5] {Petal.Width=[1.75, Inf]}  => {Species=virginica}     0.30 0.98  2.9    45 
 [6] {}                         => {Species=versicolor}    0.33 0.33  1.0   150

# 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.