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https://github.com/cran/arules
:exclamation: This is a read-only mirror of the CRAN R package repository. arules — Mining Association Rules and Frequent Itemsets. Homepage: https://github.com/mhahsler/arules Report bugs for this package: https://github.com/mhahsler/arules/issues
https://github.com/cran/arules
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:exclamation: This is a read-only mirror of the CRAN R package repository. arules — Mining Association Rules and Frequent Itemsets. Homepage: https://github.com/mhahsler/arules Report bugs for this package: https://github.com/mhahsler/arules/issues
- Host: GitHub
- URL: https://github.com/cran/arules
- Owner: cran
- Created: 2014-03-13T03:59:21.000Z (over 10 years ago)
- Default Branch: master
- Last Pushed: 2024-08-23T02:47:34.000Z (3 months ago)
- Last Synced: 2024-08-23T03:46:35.657Z (3 months ago)
- Language: R
- Homepage:
- Size: 33.5 MB
- Stars: 12
- Watchers: 6
- Forks: 5
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# 
R package arules - Mining Association Rules and Frequent Itemsets
[](https://mhahsler.r-universe.dev/arules)
[](https://CRAN.R-project.org/package=arules)
[](https://CRAN.R-project.org/package=arules)
[](https://anaconda.org/conda-forge/r-arules)
[](https://stackoverflow.com/questions/tagged/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).
To cite package ‘arules’ in publications use:
> Hahsler M, Gruen B, Hornik K (2005). “arules - A Computational
> Environment for Mining Association Rules and Frequent Item Sets.”
> *Journal of Statistical Software*, *14*(15), 1-25. ISSN 1548-7660,
> .
@Article{,
title = {arules -- {A} Computational Environment for Mining Association Rules and Frequent Item Sets},
author = {Michael Hahsler and Bettina Gruen and Kurt Hornik},
year = {2005},
journal = {Journal of Statistical Software},
volume = {14},
number = {15},
pages = {1--25},
doi = {10.18637/jss.v014.i15},
month = {October},
issn = {1548-7660},
}
## 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.
The following R packages use `arules`:
[aPEAR](https://CRAN.R-project.org/package=aPEAR),
[arc](https://CRAN.R-project.org/package=arc),
[arulesCBA](https://CRAN.R-project.org/package=arulesCBA),
[arulesNBMiner](https://CRAN.R-project.org/package=arulesNBMiner),
[arulesSequences](https://CRAN.R-project.org/package=arulesSequences),
[arulesViz](https://CRAN.R-project.org/package=arulesViz),
[clickstream](https://CRAN.R-project.org/package=clickstream),
[CLONETv2](https://CRAN.R-project.org/package=CLONETv2),
[CRE](https://CRAN.R-project.org/package=CRE),
[ctsem](https://CRAN.R-project.org/package=ctsem),
[discnorm](https://CRAN.R-project.org/package=discnorm),
[fcaR](https://CRAN.R-project.org/package=fcaR),
[fdm2id](https://CRAN.R-project.org/package=fdm2id),
[GroupBN](https://CRAN.R-project.org/package=GroupBN),
[ibmdbR](https://CRAN.R-project.org/package=ibmdbR),
[immcp](https://CRAN.R-project.org/package=immcp),
[inTrees](https://CRAN.R-project.org/package=inTrees),
[opusminer](https://CRAN.R-project.org/package=opusminer),
[pmml](https://CRAN.R-project.org/package=pmml),
[qCBA](https://CRAN.R-project.org/package=qCBA),
[RareComb](https://CRAN.R-project.org/package=RareComb),
[rattle](https://CRAN.R-project.org/package=rattle),
[rCBA](https://CRAN.R-project.org/package=rCBA),
[recommenderlab](https://CRAN.R-project.org/package=recommenderlab),
[rgnoisefilt](https://CRAN.R-project.org/package=rgnoisefilt),
[RKEEL](https://CRAN.R-project.org/package=RKEEL),
[sbrl](https://CRAN.R-project.org/package=sbrl),
[SurvivalTests](https://CRAN.R-project.org/package=SurvivalTests),
[TELP](https://CRAN.R-project.org/package=TELP)
## Installation
**Stable CRAN version:** Install from within R with
``` r
install.packages("arules")
```
**Current development version:** Install from
[r-universe.](https://mhahsler.r-universe.dev/arules)
``` r
install.packages("arules",
repos = c("https://mhahsler.r-universe.dev". "https://cloud.r-project.org/"))
```
## Usage
Load package and mine some association rules.
``` r
library("arules")
data("IncomeESL")
trans <- transactions(IncomeESL)
trans
```
## transactions in sparse format with
## 8993 transactions (rows) and
## 84 items (columns)
``` r
rules <- apriori(trans, supp = 0.1, conf = 0.9, target = "rules")
```
## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## 0.9 0.1 1 none FALSE TRUE 5 0.1 1
## maxlen target ext
## 10 rules TRUE
##
## Algorithmic control:
## filter tree heap memopt load sort verbose
## 0.1 TRUE TRUE FALSE TRUE 2 TRUE
##
## Absolute minimum support count: 899
##
## set item appearances ...[0 item(s)] done [0.00s].
## set transactions ...[84 item(s), 8993 transaction(s)] done [0.00s].
## sorting and recoding items ... [42 item(s)] done [0.00s].
## creating transaction tree ... done [0.00s].
## checking subsets of size 1 2 3 4 5 6 done [0.01s].
## writing ... [457 rule(s)] done [0.00s].
## creating S4 object ... done [0.00s].
Inspect the rules with the highest lift.
``` r
inspect(head(rules, n = 3, by = "lift"))
```
## lhs rhs support confidence coverage lift count
## [1] {dual incomes=no,
## householder status=own} => {marital status=married} 0.10 0.97 0.10 2.6 914
## [2] {years in bay area=>10,
## dual incomes=yes,
## type of home=house} => {marital status=married} 0.10 0.96 0.10 2.6 902
## [3] {dual incomes=yes,
## householder status=own,
## type of home=house,
## language in home=english} => {marital status=married} 0.11 0.96 0.11 2.6 988
## 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()
```
## # A tibble: 3 × 6
## rules support confidence coverage lift count
##
## 1 {dual incomes=no,householder status=o… 0.102 0.971 0.105 2.62 914
## 2 {years in bay area=>10,dual incomes=y… 0.100 0.961 0.104 2.59 902
## 3 {dual incomes=yes,householder status=… 0.110 0.960 0.114 2.59 988
## 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:
- Hahsler, Michael. [A Probabilistic Comparison of Commonly Used
Interest Measures for Association
Rules](https://mhahsler.github.io/arules/docs/measures), 2015, URL:
.
- 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.