Mining Sequential Purchasing Behaviors from Customer Transaction Databases

Mining sequential patterns is to find the sequential purchasing behaviors for most of the customers, which only considers the number of the customers with the purchasing behaviors in a customer transaction database. Mining high utility sequential patterns considers both of the profits and purchased quantities for the items, which is to find the sequential patterns with high benefits for the business. The previous researches for mining high utility sequential patterns roughly defined the utility of a sequence contributed by a customer, such that the generated patterns are not really high utility. Moreover, the previous approaches need to generate a large number of the candidates and scan the whole database to calculate the utilities for all the generated candidates. Therefore, in this paper, we consider the actual purchasing behaviors for the customers and exactly define the high utility sequential patterns. Besides, we also propose an efficient algorithm for mining our well-defined high utility sequential patterns which can significantly reduce the number of the candidates. The experimental results also show that our algorithm significantly outperforms the previous approach for mining high utility sequential patterns.