Discovering Multiple-Level Association Rules from Transactional Databases with Consideration of Temporal Characteristics of Products' Discounts Rates

Due to the development of information systems and technology, businesses increasingly have the capability to accumulate huge amounts of retail data in large databases. In the recent marketing research, products' discounts have rarely been considered as an important decision variable. Although few researches have analyzed the effect of discount on sales, they ignore its temporal characteristics. That is, in real world, each product may appear with different discounts rates in different time periods. Moreover, they have considered discount at single concept level. Therefore, the discovered knowledge is less concrete and implementation of the results of analyses become difficult. The problem addressed in this paper is the consideration of products' discounts in discovering multiple-level association rules in different time intervals that a specific discount appears on a specific product. The proposed algorithm makes it possible to acquire more concrete and specific knowledge corresponding to association between products and their discounts as well as implementation of its results.