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Abstract

The dynamic nature of e-commerce necessitates the adoption of cutting-edge technologies to improve the online shopping experience. Our research introduces a groundbreaking methodology called Fuzzy Association Rule Mining (FARM), combining fuzzy set theory with traditional Association Rule Mining (ARM). Unlike conventional ARM, which focuses solely on the frequency of jointly purchased items, FARM also considers the sold quantities, leveraging the Apriori algorithm to discern customer preferences from historical sales data across the UCI Online Retail II, Market Basket, and Movielens datasets. This hybrid of fuzzy set theory with ARM enables a better understanding of complicated consumer behaviors and associations between products. A variety of comprehensive statistical techniques, including support, confidence, lift metrics, Chi-square test, and P-value hypothesis test, are applied in our approach to validating the statistical robustness of the mined rules. The result shows that FARM offers a broader range of insight into product associations and markedly enhances customer recommendations' precision and relevance. The above-described innovation is a great addition to any e-commerce portal that aims to refine its recommendation system, and it also captures an aspect of more granularity in consumer preference. Through this work, we aspire to contribute to the academic discourse on data mining by presenting a robust analytical framework that gives a finer definition to the subtle nuances underlying their buying behavior. We debate and highlight how important it is to merge these statistical methods with data mining in e-commerce recommendation systems to improve them by carefully analyzing transactional data.

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