Market Basket Analysis Using Rule-Based Algorithms and Data Mining Techniques

TL;DR

This paper presents a framework for applying rule-based data mining algorithms to transactional data for market basket analysis, aiming to improve marketing strategies and operational efficiency.

Contribution

It introduces a structured methodology for extracting meaningful association rules from transactional data to support managerial decision-making.

Findings

Identification of significant association rules

Enhanced understanding of customer purchasing patterns

Potential for improved marketing strategies

Abstract

The research identifies association rules that can inform marketing strategies and enhance operational efficiency. A structured methodology is applied to extract and interpret meaningful relationships within transactional data, emphasizing their implications for managerial decision-making. By demonstrating the potential of data mining to transform raw data into valuable business insights, this paper provides a framework for using analytical tools to improve customer engagement and competitive positioning.