Association rule mining is a popular data mining method to discover interesting relation ships between variables in large databases. An extensive toolbox is available in the R-extension package arules. However, mining association rules often results in a vast number of found rules, leaving the analyst with the task to go through a large set of rules to identify interesting ones. Sifting manually through extensive sets of rules is time-consuming and strenuous. Visualization and espe cially interactive visualization has a long history of making large amounts of data better accessible. The R-extension package arulesViz provides most popular visualization techniques for association rules. In this paper, we discuss recently added interactive visualizations to explore association rules and demonstrate how easily they can be used in arulesViz via a unified interface. With examples, we help to guide the user in selecting appropriate visualizations and interpreting the results.
arulesViz, arules, DT, plotly, grid, visNetwork
MachineLearning, ReproducibleResearch
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For attribution, please cite this work as
Hahsler, "The R Journal: arulesViz: Interactive Visualization of Association Rules with R", The R Journal, 2017
BibTeX citation
@article{RJ-2017-047,
author = {Hahsler, Michael},
title = {The R Journal: arulesViz: Interactive Visualization of Association Rules with R},
journal = {The R Journal},
year = {2017},
note = {https://doi.org/10.32614/RJ-2017-047},
doi = {10.32614/RJ-2017-047},
volume = {9},
issue = {2},
issn = {2073-4859},
pages = {163-175}
}