Efficient mining of weighted frequent itemsets using MLWFI

Efficient algorithms for mining weighted frequent itemsets are crucial for mining weighted association rules. However, the use of frequent itemsets has been limited by the high computational cost. Meanwhile, the "downward closure property" is invalid in the weighted association rule mining model. In this paper, we define a new problem of finding the weighted frequent itemsets with a maximum length (MLWFL) and present a novel algorithm to solve these problems. Our methods are scalable and efficient in discovering significant relationships in weighted settings as illustrated by experiments performed on simulated datasets.