Mining Changes of E-shopper Purchase Behavior in B2C

In the Internet shopping environment, changes of customer´s needs grow increasingly outstanding. For discovering the changes, the paper mines the transaction databases of different time periods by using association rule discovery, and extracts the association rules and discovers the changes in network customer behavior by comparison and analysis between the two sets of association rules. This paper presents a new algorithm that contains the first support, the second support and Rel-confidence. The algorithm solves the problem of the present algorithms cannot discover association rules with infrequent data items. And according to the changes of network customer´s behavior, robust pattern, appearing pattern and unexpected change are presented to measure the changes.