This repository implements the Apriori algorithm to mine association rules from transaction data. The code is flexible and can be used with any dataset containing transactional data.
The task involves analyzing transaction data to uncover associations between different items. Transactions are represented by sets of items, with each item appearing in a separate row under the same transaction ID. The goal is to identify frequent item sets and generate strong association rules based on user-defined parameters.
A dataset in Excel, CSV, or text file format. Minimum support count. Minimum confidence (percentage value).
The frequent itemsets. Strong association rules with their confidence.
User-friendly interface that allows the user to select the percentage of the data to be analyzed. The algorithm used is Apriori.