A Framework for Weighted Association Rule Mining from Boolean and Fuzzy Data

Association rules mining is one of the most important tasks in the field of data mining. It aims at searching for interesting relationship among items in a large data set. In this paper, we present a novel approach for mining the fuzzy weighted association rule from boolean and fuzzy data in large data set, where a weighted value is assigned to each item, we develop a novel approach to calculate the support and confidence of the weighted items, experimental results show that the proposed method is efficient and scalable.