Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/ianstenbit/arulescba

Classification Based on Association Rules in R
https://github.com/ianstenbit/arulescba

algorithm association-rules cba classification cran r

Last synced: 16 days ago
JSON representation

Classification Based on Association Rules in R

Awesome Lists containing this project

README 

        
---

title: "R package arulesCBA: Classification Based on Association Rules"

output: github_document

---



```{r echo=FALSE}

options(digits = 2)

knitr::opts_chunk$set(tidy = TRUE, message = FALSE)

```



```{r echo=FALSE, results = 'asis'}

pkg <- 'arulesCBA'



library(stringr)



  cat(str_interp("[![CRAN version](http://www.r-pkg.org/badges/version/${pkg})](https://CRAN.R-project.org/package=${pkg})\n"))

  cat(str_interp("[![stream r-universe status](https://mhahsler.r-universe.dev/badges/${pkg})](https://mhahsler.r-universe.dev/ui#package:${pkg})\n"))

  cat(str_interp("[![CRAN RStudio mirror downloads](http://cranlogs.r-pkg.org/badges/grand-total/${pkg})](https://CRAN.R-project.org/package=${pkg})\n"))

```



The R package [arulesCBA](https://cran.r-project.org/package=arulesCBA) (Hahsler et al, 2020) 

is an extension of the package [arules](https://cran.r-project.org/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

```{r eval=FALSE}

install.packages("arulesCBA")

```



__Current development version:__ Install from [r-universe.](https://mhahsler.r-universe.dev/ui#package:arulesCBA)



## Usage



```{r}

library("arulesCBA")

data("iris")

```



Learn a classifier.



```{r}

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

classifier

```



Inspect the rulebase.



```{r}

inspect(rules(classifier), linebreak = TRUE)

```

  

Make predictions for the first few instances of iris.



```{r}

predict(classifier, head(iris))

```



## References



* M. Hahsler, I. Johnson, T. Kliegr and J. Kuchar (2019). [Associative Classification in R: arc, arulesCBA, and rCBA](https://journal.r-project.org/archive/2019/RJ-2019-048/). _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.