Market Basket Analysis: Identify the Changing Trends of Market Data Using Association Rule Mining

TLDR

Market Basket Analysis is a data mining technique used across fields such as marketing, bioinformatics, education, and nuclear science, yet existing algorithms operate on static data and fail to capture temporal changes. This study aims to develop a new association rule mining algorithm that captures temporal changes in market data to help retailers understand customer behavior and boost sales. The authors propose a novel association rule mining algorithm that simultaneously analyzes static and evolving market data to uncover changing purchase patterns.

Abstract

Market Basket Analysis(MBA) also known as association rule learning or affinity analysis, is a data mining technique that can be used in various fields, such as marketing, bioinformatics, education field, nuclear science etc. The main aim of MBA in marketing is to provide the information to the retailer to understand the purchase behavior of the buyer, which can help the retailer in correct decision making. There are various algorithms are available for performing MBA. The existing algorithms work on static data and they do not capture changes in data with time. But proposed algorithm not only mine static data but also provides a new way to take into account changes happening in data. This paper discusses the data mining technique i.e. association rule mining and provide a new algorithm which may helpful to examine the customer behaviour and assists in increasing the sales.