Pattern Discovery and Analysis of Customer Buying Behavior Using Association Rules Mining Algorithm in E-Commerce

Abstract

Customers are important in the business sector because they buy and sell products or use services. Customers typically switch between buying sites based on which one offers the best deal. To keep loyal customers, the business owner must understand their purchasing habits and mindset. A customer's purchased items or requirements are gathered from various sources and analyzed using appropriate data mining techniques. This analysis is used to determine the customer's purchasing pattern, which leads to a better understanding of their needs and purchasing behavior. The discovered pattern also aids in inventory management, product development, and service delivery. This paper examines E-Commerce datasets in order to discover useful and interesting patterns by employing data mining association rules to achieve the best results. The rules were generated by the FP-Growth algorithm from frequently used item sets. The research is implemented in the rapid miner tool and evaluated using appropriate evaluation metrics.