R package arules - Mining Association Rules and Frequent Itemsets R package arules - Mining Association Rules and Frequent ItemsetsMaintainer: Michael Hahsler
The arules package family for R provides the infrastructure for representing, manipulating and analyzing transaction data and patterns using frequent itemsets and association rules. The package also provides a wide range of interest measures and mining algorithms including the code of Christian Borgelt’s popular and efficient C implementations of the association mining algorithms Apriori and Eclat. In addition, the following mining algorithms are available via fim4r:
Code examples can be found in Chapter 5 of the web book R Companion for Introduction to Data Mining.
Additional mining algorithms
fim4r()
is provided in arules.opus() with format = 'itemsets'.In-database analytics
Interface
Classification
Recommendation/Prediction
The following R packages use arules:
arc,
arlclustering,
arulesCBA,
arulesNBMiner,
arulesSequences,
arulesViz,
clickstream,
CLONETv2,
CRE,
ctsem,
daltoolbox,
discnorm,
fcaR,
fdm2id,
gp3sequences,
GroupBN,
ibmdbR,
icdhelper,
manydist,
nuggets,
opusminer,
pervasive,
pmml,
qCBA,
RareComb,
rattle,
rCBA,
recommenderlab,
rgnoisefilt,
RulesTools,
sbrl,
SurvivalTests,
TELP,
tidylearn
Stable CRAN version: Install from within R with
install.packages("arules")
Current development version: Install from r-universe.
install.packages("arules",
repos = c("https://mhahsler.r-universe.dev",
"https://cloud.r-project.org/"))
Load package and mine some association rules.
library("arules")
data("IncomeESL")
trans <- transactions(IncomeESL)
trans
## transactions in sparse format with
## 8993 transactions (rows) and
## 84 items (columns)
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 [0.01s].
## 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 [0.02s].
## writing ... [457 rule(s)] done [0.00s].
## creating S4 object ... done [0.00s].
Inspect the rules with the highest lift.
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
The following short guides cover the main workflows:
Using arules with other tidyverse or Python:
The theoretical background and the original description of the package are available in the package vignette Introduction to arules.
Please report bugs here on GitHub. Questions should be posted on stackoverflow and tagged with arules.
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. doi:10.18637/jss.v014.i15 https://doi.org/10.18637/jss.v014.i15.
@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},
}