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R package arulesViz - Visualizing Association Rules and Frequent Itemsets

Introduction

This R package extends package arules with various visualization techniques for association rules and itemsets. The package also includes several interactive visualizations for rule exploration.

The following R packages use arulesViz: arules, fdm2id, rattle, TELP

To cite package ‘arulesViz’ in publications use:

Hahsler M (2017). “arulesViz: Interactive Visualization of Association Rules with R.” R Journal, 9(2), 163-175. ISSN 2073-4859, doi:10.32614/RJ-2017-047 https://doi.org/10.32614/RJ-2017-047, https://journal.r-project.org/archive/2017/RJ-2017-047/RJ-2017-047.pdf.

@Article{,
  title = {arules{V}iz: {I}nteractive Visualization of Association Rules with {R}},
  author = {Michael Hahsler},
  year = {2017},
  journal = {R Journal},
  volume = {9},
  number = {2},
  pages = {163--175},
  url = {https://journal.r-project.org/archive/2017/RJ-2017-047/RJ-2017-047.pdf},
  doi = {10.32614/RJ-2017-047},
  month = {December},
  issn = {2073-4859},
}

This might also require the development version of arules.

Features

• Visualizations using engines ggplot2 (default engine for most methods), grid, base (R base plots), htmlwidget (powered by plotly and visNetwork).
• Interactive visualizations using grid, plotly and visNetwork.
• Interactive rule inspection with datatable.
• Integrated interactive rule exploration using ruleExplorer.

Available Visualizations

• Scatterplot, two-key plot
• Matrix and matrix 3D visualization
• Grouped matrix-based visualization
• Several graph-based visualizations
• Doubledecker and mosaic plots
• Parallel Coordinate plot

Installation

Stable CRAN version: Install from within R with

install.packages("arulesViz")

Current development version: Install from r-universe.

install.packages("arulesViz",
    repos = c("https://mhahsler.r-universe.dev". "https://cloud.r-project.org/"))

Usage

Mine some rules.

library("arulesViz")
data("Groceries")
rules <- apriori(Groceries, parameter = list(support = 0.005, confidence = 0.5))

## Apriori
## 
## Parameter specification:
##  confidence minval smax arem  aval originalSupport maxtime support minlen
##         0.5    0.1    1 none FALSE            TRUE       5   0.005      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: 49 
## 
## set item appearances ...[0 item(s)] done [0.00s].
## set transactions ...[169 item(s), 9835 transaction(s)] done [0.00s].
## sorting and recoding items ... [120 item(s)] done [0.00s].
## creating transaction tree ... done [0.00s].
## checking subsets of size 1 2 3 4 done [0.00s].
## writing ... [120 rule(s)] done [0.00s].
## creating S4 object  ... done [0.00s].

Standard visualizations

plot(rules)

plot(rules, method = "graph", limit = 20)

Interactive visualization

Live examples for interactive visualizations can be seen in Chapter 5 of An R Companion for Introduction to Data Mining

References

• Michael Hahsler. arulesViz: Interactive visualization of association rules with R. R Journal, 9(2):163-175, December 2017.
• Michael Hahsler. An R Companion for Introduction to Data Mining: Chapter 5. Online Book. https://mhahsler.github.io/Introduction_to_Data_Mining_R_Examples/book/,
• Michael Hahsler, Sudheer Chelluboina, Kurt Hornik, and Christian Buchta. The arules R-package ecosystem: Analyzing interesting patterns from large transaction datasets. Journal of Machine Learning Research, 12:1977-1981, 2011.
• Michael Hahsler and Sudheer Chelluboina. Visualizing Association Rules: Introduction to the R-extension Package arulesViz (with complete examples).

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.