In the electronic commerce environment, the consumers can purchase products from the internet and the business can easily obtain the data about the transactions. Compared with other commodities, consumable products are purchased high-frequently. Although single gains for consumable products may be lower than that of appliances or electronic products, the accumulative gains for consumable products are great. Therefore, grasping suitable timing to do sales promotion for consumable products is an important task. For the consumable products, the purchase time for the next transaction is usually related to the purchased quantities for this transaction. In this paper, we propose a novel data mining algorithm to find the item-consumption behaviors for most of the consumers. From this information, we can predict the next purchase time for an item based on the purchased quantity of this item at this time. The experimental results show that our algorithm is efficient and scalable, and the mining results can exactly reflect the consumption behaviors for most of the consumers.
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