Using Pattern-Join and Purchase-Combination for Mining Web Transaction Patterns in an Electronic Commerce Environment

In this paper, we explore the data mining capability which involves mining Web transaction patterns for an electronic commerce (EC) environment. To better reflect the customer usage patterns in the EC environment, we propose a mining model that takes both the traveling patterns and purchasing behavior of customers into consideration. We devise two efficient algorithms (MTSPJ , and MTSPC) for determining the frequent transaction patterns, which are termed large transaction patterns in this paper. In addition, algorithm WTM devised in our prior work, is used for comparison purposes. By utilizing the path-trimming technique which is developed to exploit the relationship between traveling and purchasing behaviors, MTSPJ and MTSPC are able to generate the large transaction patterns very efficiently. A simulation model for the EC environment is developed and a synthetic workload is generated for performance studies.