Discovering consumer's behavior changes based on purchase sequences

In the Internet shopping environment, changes of customer's needs grow increasingly outstanding. One of the most important marketing resources is the prior daily transaction records in the consumer's database. In this study, the paper present a new methodology for predicting consumers' purchase behavior that uses consumers' purchase sequences. First, transaction clustering is conducted, then it is made that detecting the evolving consumer purchase sequences as time passes, and the consumers behaviors, which are derived from a change in the cluster number of each consumer, are kept in the purchase sequence database. Finally, sequential purchase patterns over user-specified minimum support and confidence are extracted by using the association rule. The sequential purchase patterns are then stored in the association rule database. The better result is achieved by applying the new methodogy to a given example for consumers.