Eclat — Market Basket Analysis

Frequent-itemset mining on grocery transaction data using the Eclat algorithm in R.

What It Does

Eclat (Equivalence Class Transformation) discovers which products are frequently purchased together by scanning a dataset of 7 500 grocery transactions. Unlike Apriori, Eclat uses a depth-first search with set-intersection counting, which is often faster on dense datasets.

Workflow

1. Load transactions from CSV using arules::read.transactions()
2. Mine frequent itemsets with a minimum support of 0.3 % and a minimum length of 2
3. Rank results by support and display the top 10

Dataset

File	Rows	Description
Market_Basket_Optimisation.csv	7 501	Each row is one transaction; items are comma-separated

🛠 Tech Stack

	Tool	Purpose
📊	R	Statistical computing
📦	arules	Association rule / frequent-itemset mining

Getting Started

# Install the dependency (once)
install.packages("arules")

# Run the script
source("eclat.R")

⚠️ Known Issues

• The CSV has no header row; read.transactions() handles this correctly, but read.csv() would misinterpret the first transaction as column names.