mhahsler/arulesviz

Visualizing Association Rules and Frequent Itemsets with R

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/, 2021.
• 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).