# arulesCBA v1.1.3-1

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## Classification Based on Association Rules

Provides a function to build an association rule-based classifier for data frames, and to classify incoming data frames using such a classifier.

# Classification Based on Association Rules

This R package is an extension of the package arules to perform association rule-based classification. It includes currently two classification algorithms. The first is the CBA algorithm described in Liu, et al. 1998. The second is a new weighted majority-vote based algorithm called bCBA which is currently being designed and tested. Time-critical sections of the code are implemented in C.

The package also provides support for supervised discretization and mining Class Association Rules (CARs).

## 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 using automatic default discretization
classifier <- CBA(Species ~ ., data = iris, supp = 0.05, conf = 0.9)
classifier

CBA Classifier Object
Class: Species=setosa, Species=versicolor, Species=virginica
Default Class: Species=setosa
Number of rules: 8
Classification method: first
Description: CBA algorithm by Liu, et al. 1998 with support=0.05 and confidence=0.9

# make predictions for the first few instances of iris

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


## References

• Liu, B. Hsu, W. and Ma, Y (1998). Integrating Classification and Association Rule Mining. KDD'98 Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining, New York, 27-31 August. AAAI. pp. 80-86.

## Functions in arulesCBA

 Name Description bCBA Classification Based on Association Rules CBA Classification Based on Association Rules Algorithm (CBA) CBA.object Objects for Classifiers Based on Association Rules mineCARs Mine Class Association Rules wCBA Classification Based on Association Rules discretizeDF.supervised Supervised Methods to Convert Continuous Variables into Categorical Variables No Results!