Fuzzy association rule mining approach to identify e-commerce product association considering sales amount

Onur Dogan,Basar Oztaysi,Furkan Can Kem

doi:10.1007/s40747-021-00607-3

Abstract

Online stores assist customers in buying the desired products online. Great competition in the e-commerce sector necessitates technology development. Many e-commerce systems not only present products but also offer similar products to increase online customer interest. Due to high product variety, analyzing products sold together similar to a recommendation system is a must. This study methodologically improves the traditional association rule mining (ARM) method by adding fuzzy set theory. Besides, it extends the ARM by considering not only items sold but also sales amounts. Fuzzy association rule mining (FARM) with the Apriori algorithm can catch the customers’ choice from historical transaction data. It discovers fuzzy association rules from an e-commerce company to display similar products to customers according to their needs in amount. The experimental result shows that the proposed FARM approach produces much information about e-commerce sales for decision-makers. Furthermore, the FARM method eliminates some traditional rules considering their sales amount and can produce some rules different from ARM.

Highlights

Electronic commerce (e-commerce) has gained increasing popularity, because the Internet becomes an essential tool for companies to increase their competitive edge by collecting and analyzing customer data
This study aims to present the potential of using the Fuzzy association rule mining (FARM) approach with Apriori algorithm for e-commerce sales
The proposed FARM methodology considers the sales amount, whereas the traditional association rule mining (ARM) method takes into account binary variables, sold or not

Summary

Introduction

Electronic commerce (e-commerce) has gained increasing popularity, because the Internet becomes an essential tool for companies to increase their competitive edge by collecting and analyzing customer data. Apriori algorithm overcomes the problem of a high number of data attributes It diminishes the number of possible items to produce frequent association rules. The main contribution of the study is to recommend more relevant products to online customers by considering items sold and sales amounts. Previous studies such as [1,14] applied in e-commerce focused on items sold together in a certain period. The results of the study support decision-makers deciding how many and which kinds of products to generate the closest attention from online customers. This research proposes a methodology extending association rule mining with fuzzy set theory for this motivation. “Conclusion and limitations” concludes the study by discussing the results and giving the limitations of the proposed methodology

Literature review

Evaluation of the proposed methodology

Methods*