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https://github.com/mhahsler/arules

Mining Association Rules and Frequent Itemsets with R
https://github.com/mhahsler/arules

arules association-rules cran frequent-itemsets r

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Mining Association Rules and Frequent Itemsets with R

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README 

        
---

output: github_document

---



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

pkg <- 'arules'



source("https://raw.githubusercontent.com/mhahsler/pkg_helpers/main/pkg_helpers.R")

pkg_title(pkg, anaconda = "r-arules", stackoverflow = "arules")

```



## Introduction



The arules package family for R provides the infrastructure for representing,

manipulating and analyzing transaction data and patterns

using [frequent itemsets and association rules](https://en.wikipedia.org/wiki/Association_rule_learning).

The package also provides a wide range of 

[interest measures](https://mhahsler.github.io/arules/docs/measures) and mining algorithms including the code of

Christian Borgelt's popular and efficient C implementations of the association mining algorithms [Apriori](https://borgelt.net/apriori.html) and [Eclat](https://borgelt.net/eclat.html). In addition, the following mining algorithms are

available via [fim4r](https://borgelt.net/fim4r.html):



* Apriori

* Eclat

* Carpenter

* FPgrowth

* IsTa 

* RElim 

* SaM



Code examples can be found in

[Chapter 5 of the web book R Companion for Introduction to Data

Mining](https://mhahsler.github.io/Introduction_to_Data_Mining_R_Examples/book/association-analysis-basic-concepts-and-algorithms.html).



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

pkg_citation(pkg, 2)

```



## Packages



### arules core packages



* [arules](https://cran.r-project.org/package=arules): arules base package with data structures, mining algorithms (APRIORI and ECLAT), interest measures. 

* [arulesViz](https://github.com/mhahsler/arulesViz): Visualization of association rules. 

* [arulesCBA](https://github.com/ianstenbit/arulesCBA): Classification algorithms based on association rules (includes CBA).  

* [arulesSequences](https://cran.r-project.org/package=arulesSequences): Mining frequent sequences (cSPADE).



### Other related packages



Additional mining algorithms 



* [arulesNBMiner](https://github.com/mhahsler/arulesNBMiner): Mining NB-frequent itemsets and NB-precise rules.

* [fim4r](https://borgelt.net/fim4r.html): Provides fast implementations for several mining algorithms. An interface function called `fim4r()` is provided in `arules`.

* [opusminer](https://cran.r-project.org/package=opusminer): OPUS Miner algorithm for finding the op k productive, non-redundant itemsets. Call `opus()` with `format = 'itemsets'`. 

* [RKEEL](https://cran.r-project.org/package=RKEEL): Interface to KEEL's association rule mining algorithm.

* [RSarules](https://cran.r-project.org/package=RSarules): Mining algorithm which randomly samples association rules with one pre-chosen item as the consequent from a transaction dataset.



In-database analytics



* [ibmdbR](https://cran.r-project.org/package=ibmdbR): IBM in-database analytics for R can calculate association rules from a database table.

* [rfml](https://cran.r-project.org/package=rfml): Mine frequent itemsets or association rules using a MarkLogic server. 



Interface



* [rattle](https://cran.r-project.org/package=rattle): Provides a graphical user interface for association rule mining.

* [pmml](https://cran.r-project.org/package=pmml): Generates PMML (predictive model markup language) for association rules.



Classification 



* [arc](https://cran.r-project.org/package=arc): Alternative CBA implementation. 

* [inTrees](https://cran.r-project.org/package=inTrees): Interpret Tree Ensembles provides functions for: extracting, measuring and pruning rules; selecting a compact rule set; summarizing rules into a learner.

* [rCBA](https://cran.r-project.org/package=rCBA): Alternative CBA implementation.

* [qCBA](https://cran.r-project.org/package=qCBA): Quantitative Classification by Association Rules.

* [sblr](https://cran.r-project.org/package=sbrl): Scalable Bayesian rule lists algorithm for classification.



Outlier Detection



* [fpmoutliers](https://cran.r-project.org/package=fpmoutliers): Frequent Pattern Mining Outliers.



Recommendation/Prediction



* [recommenerlab](https://github.com/mhahsler/recommenderlab): Supports creating predictions using association rules.



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

pkg_usage(pkg)

```



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

pkg_install(pkg)

```



## Usage



Load package and mine some association rules. 

```{r }

library("arules")

data("IncomeESL")



trans <- transactions(IncomeESL)

trans



rules <- apriori(trans, supp = 0.1, conf = 0.9, target = "rules")

```



Inspect the rules with the highest lift.

```{r }

inspect(head(rules, n = 3, by = "lift"))

```



## Using arules with tidyverse



`arules` works seamlessly with [tidyverse](https://www.tidyverse.org/). For example: 



* `dplyr` can be used for cleaning and preparing the transactions.

* `transaction()` and other functions accept `tibble` as input.

* Functions in arules can be connected with the pipe operator `|>`.

* [arulesViz](https://github.com/mhahsler/arulesViz) provides visualizations based on `ggplot2`.



For example, we can remove the ethnic information column before creating transactions and then mine and inspect rules.

```{r }

library("tidyverse")

library("arules")

data("IncomeESL")



trans <- IncomeESL |> 

      select(-`ethnic classification`) |> 

      transactions()

rules <- trans |> 

      apriori(supp = 0.1, conf = 0.9, target = "rules", 

              control = list(verbose = FALSE))

rules |> 

      head(3, by = "lift") |>

      as("data.frame") |> 

      tibble()

```



## Using arules from Python



`arules` and `arulesViz` can now be used directly from Python with the Python 

package [`arulespy`](https://pypi.org/project/arulespy/) available form PyPI. 



## Support



Please report bugs [here on GitHub.](https://github.com/mhahsler/arules/issues)

Questions should be posted on [stackoverflow and tagged with arules](https://stackoverflow.com/questions/tagged/arules).



## References



* Michael Hahsler. [ARULESPY: Exploring association rules and frequent itemsets in 

  Python.](http://dx.doi.org/10.48550/arXiv.2305.15263) arXiv:2305.15263 [cs.DB], May 2023.

* Michael Hahsler. [An R Companion for Introduction to Data Mining: Chapter 5](https://mhahsler.github.io/Introduction_to_Data_Mining_R_Examples/book/association-analysis-basic-concepts-and-algorithms.html), 2021, URL: https://mhahsler.github.io/Introduction_to_Data_Mining_R_Examples/book/

* Hahsler, Michael. [A Probabilistic Comparison of Commonly Used Interest Measures for Association Rules](https://mhahsler.github.io/arules/docs/measures), 2015, URL: https://mhahsler.github.io/arules/docs/measures.

* Michael Hahsler, Sudheer Chelluboina, Kurt Hornik, and Christian Buchta. [The arules R-package ecosystem: Analyzing interesting patterns from large transaction datasets.](https://jmlr.csail.mit.edu/papers/v12/hahsler11a.html) _Journal of Machine Learning Research,_ 12:1977-1981, 2011.

* Michael Hahsler, Bettina Grün and Kurt Hornik. [arules - A Computational Environment for Mining Association Rules and Frequent Item Sets.](https://dx.doi.org/10.18637/jss.v014.i15) _Journal of Statistical Software,_ 14(15), 2005.