Abstract: In order to detect users’ abnormal trading behavior in e-commerce, users’ behavioral log data were firstly classified into two categories, namely static attribute sets and operational sequence sets respectively. Then Apriori algorithm based on axis attribute and GSP algorithm for mining sequential patterns were used on these two different data sets, and users’ normal behavior patterns were then established. Finally, user's current behavior patterns and their past normal behavior patterns were compared using pattern matching method based on sequence, and then one could judge whether the user’s trading behavior is normal or not. The experiment on real data sets shows that the method could effectively detect users’ abnormal behavior in e-commerce trading.

Key words: electronic commerce, abnormal behavior, axis attribute, pattern mining, GSP