Abstract
Mining frequent itemsets (FIs) is an important problem in the field of data mining, and thus there have been many different methods proposed to solve this problem. However, mining FIs usually works on binary databases and has a limitation that is only concerned with the appearance of items regardless of their importance. In practical applications, items often have different importance depending on their values or meanings, and that leads to the emergence of weighted databases. In this paper, we propose a new method for mining frequent weighted itemsets (FWIs) from a weighted database by using the weighted N-list structure (WN-list), an extension of the N-list. Some theorems are proposed to calculate the weighted supports of itemsets fast, and then an algorithm is built based on these theorems for efficiently mining FWIs. The experimental results show that the proposed method outperforms existing methods, especially when run on very large and sparse databases.
Published Version
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