A Framework for Weighted Association Rule Mining from Boolean and Fuzzy Data
Guangyuan Li, Qinbin Hu · 2011
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.