Abstract
In this paper, we propose algorithms for mining Frequent Weighted Itemsets (FWIs) from weighted items transaction databases. Firstly, we introduce the WIT-tree data structure for mining high utility itemsets in the work of Le et al. (2009) and modify it for mining FWIs. Next, some theorems are proposed. Based on these theorems and the WIT-tree, we propose an algorithm for mining FWIs. Finally, Diffset for fast computing the weighted support of itemsets and saving memory are also discussed. We test the proposed algorithms in many databases and experimental results show that they are very efficient in comparison with Apriori-based approach
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