In mhahsler/arules: Mining Association Rules and Frequent Itemsets

pkg <- 'arules'

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

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. 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:

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.

pkg_citation(pkg, 2)

Packages

arules core packages

arules: arules base package with data structures, mining algorithms (APRIORI and ECLAT), interest measures.
arulesViz: Visualization of association rules.
arulesCBA: Classification algorithms based on association rules (includes CBA).
arulesSequences: Mining frequent sequences (cSPADE).

Other related packages

Additional mining algorithms

arulesNBMiner: Mining NB-frequent itemsets and NB-precise rules.
fim4r: Provides fast implementations for several mining algorithms. An interface function called fim4r() is provided in arules.
opusminer: OPUS Miner algorithm for finding the op k productive, non-redundant itemsets. Call opus() with format = 'itemsets'.
RKEEL: Interface to KEEL's association rule mining algorithm.
RSarules: Mining algorithm which randomly samples association rules with one pre-chosen item as the consequent from a transaction dataset.

In-database analytics

ibmdbR: IBM in-database analytics for R can calculate association rules from a database table.
rfml: Mine frequent itemsets or association rules using a MarkLogic server.

Interface

rattle: Provides a graphical user interface for association rule mining.
pmml: Generates PMML (predictive model markup language) for association rules.

Classification

arc: Alternative CBA implementation.
inTrees: Interpret Tree Ensembles provides functions for: extracting, measuring and pruning rules; selecting a compact rule set; summarizing rules into a learner.
rCBA: Alternative CBA implementation.
qCBA: Quantitative Classification by Association Rules.
sblr: Scalable Bayesian rule lists algorithm for classification.

Outlier Detection

fpmoutliers: Frequent Pattern Mining Outliers.

Recommendation/Prediction

recommenerlab: Supports creating predictions using association rules.

pkg_usage(pkg)

pkg_install(pkg)

Usage

Load package and mine some association rules.

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.

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

Using arules with tidyverse

arules works seamlessly with tidyverse. 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 provides visualizations based on ggplot2.

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

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()