Abstract
Weighted itemset mining has been a widely studied topic in data mining. The reason is that weighted itemset mining considers not only the occurrence of items in transactions but also the individual importance of items. The traditional upper-bound model can be used to handle the weighted itemset mining problem, but a large number of candidates are generated by the model. This work thus presents an improved model to enhance the effectiveness of reducing unpromising candidates. Besides, an effective strategy, projection-based pruning, is proposed as well to tighten upper-bounds of weighted supports for itemsets in the mining process, thus reducing the execution time further. Through a series of experimental evaluation, the results on synthetic and real datasets show that the proposed approach has good performance in both pruning effectiveness and execution efficiency under various parameter settings when compared to some other approaches.