Market Basket Analysis Using Association Rules

Saturday, November 15, 2008

The original post has been migrated from Excel/XLMiner, migrated from http://glue.umd.edu/~mmallari/ (sunsetted), and refreshed with newer datasets and references.

Association rules mining is significant in discovering meaningful relationships and patterns within large datasets. It is widely used for market basket analysis, where it identifies frequently co-occurring items in transactions. Association rules help uncover purchasing patterns, optimize product placement, and enable targeted marketing strategies. Additionally, they find applications in recommendation systems, fraud detection, web mining, and healthcare analytics. Association rules provide insights into the associations between items, allowing businesses to make data-driven decisions, personalize customer experiences, and improve operational efficiency. Their ability to uncover hidden connections in transactional data makes association rules a valuable tool for understanding customer behavior and optimizing business processes.

Let’s apply this technique to the X dataset.

Getting Started

If you are interested in reproducing this work, here are the versions of R, Python, and Julia used (as well as the respective packages for each). Additionally, Leland Wilkinson’s approach to data visualization (Grammar of Graphics) has been adopted for this work. Finally, my coding style here is verbose, in order to trace back where functions/methods and variables are originating from, and make this a learning experience for everyone—including me.

cat(R.version$version.string, R.version$nickname)

R version 4.2.3 (2023-03-15) Shortstop Beagle

require(devtools)
devtools::install_version("dplyr", version="1.1.2", repos="http://cran.us.r-project.org")
devtools::install_version("ggplot2", version="3.4.2", repos="http://cran.us.r-project.org")

library(dplyr)
library(ggplot2)

Python

import sys
print(sys.version)

3.11.4 (v3.11.4:d2340ef257, Jun  6 2023, 19:15:51) [Clang 13.0.0 (clang-1300.0.29.30)]

!pip install pandas==2.0.3
!pip install plotnine==0.12.1

import pandas
import plotnine

Julia

using InteractiveUtils
InteractiveUtils.versioninfo()

Julia Version 1.9.2
Commit e4ee485e909 (2023-07-05 09:39 UTC)
Platform Info:
  OS: macOS (x86_64-apple-darwin22.4.0)
  CPU: 8 × Intel(R) Core(TM) i5-8259U CPU @ 2.30GHz
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-14.0.6 (ORCJIT, skylake)
  Threads: 1 on 8 virtual cores
Environment:
  DYLD_FALLBACK_LIBRARY_PATH = /Library/Frameworks/R.framework/Resources/lib:/Library/Java/JavaVirtualMachines/jdk1.8.0_241.jdk/Contents/Home/jre/lib/server

using Pkg
Pkg.add(name="CSV", version="0.10.11")
Pkg.add(name="DataFrames", version="1.5.0")
Pkg.add(name="CategoricalArrays", version="0.10.8")
Pkg.add(name="Colors", version="0.12.10")
Pkg.add(name="Cairo", version="1.0.5")
Pkg.add(name="Gadfly", version="1.3.4")

using CSV
using DataFrames
using CategoricalArrays
using Colors
using Cairo
using Gadfly

Importing and Examining Dataset

References

James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). An Introduction to Statistical Learning: With Applications in R (2nd ed.). Springer. https://doi.org/10.1007/978-1-0716-1418-1
Shmueli, G., Patel, N. R., & Bruce, P. C. (2007). Data Mining for Business Intelligence. Wiley.
Albright, S. C., Winston, W. L., & Zappe, C. (2003). Data Analysis for Managers with Microsoft Excel (2^nd ed.). South-Western College Publishing.

Applied Advanced Analytics & AI in Sports