Abstract
Explores a data mining capability which involves mining Web transaction in an electronic commerce (EC) environment. To better reflect the customer usage in the EC environment, we propose a data mining model that takes both the customers' travelling and their purchasing behaviour into consideration. We devise two efficient algorithms [MTS/sub PJ/ (Maximal Transaction Segment with Pattern Join) and MTS/sub PC/ (Maximal Transaction Segment with Purchase Combination)] for determining frequent transaction patterns, which are termed transaction patterns in this paper. In addition, the WTM (Web Transaction Mining) algorithm is used for comparison purposes. By utilizing the path-trimming technique, which is developed to exploit the relationship between travelling and purchasing behaviours, MTS/sub PJ/ and MTS/sub PC/ are able to generate the large transaction very efficiently. A simulation model for the EC environment is developed and a synthetic workload is generated for performance studies.
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