Mining web transaction patterns in an electronic commerce environment

Abstract

Association rule mining discovers most of the users' purchasing behaviors from transaction database. Association rules are valuable for cross-marking and attached mailing applications. Other applications include catalog design, add-on sales, store layout, and customer segmentation based on buying patterns. Web traversal pattern mining discovers most of the users' access patterns from web logs. This information can provide navigation suggestions for web users such that appropriate actions can be adopted. Web transaction pattern mining discovers not only the pure navigation behaviors but also the purchasing behaviors of customers. In this paper, we propose an algorithm IWA (Integrating Web traversal patterns and Association rules) for mining web transaction patterns in the electronic commerce environment. Our IWA algorithm takes both the traveling and purchasing behaviors of customers into consideration at the same time. The experimental results show that IWA algorithm can simultaneously and efficiently discover traveling and purchasing behaviors for most of customers. © Springer-Verlag Berlin Heidelberg 2007.