Finding the large item set fast is the crucial step in the association rule algorithm. In this paper we apply granular computing and quotient space to frequent item set discovering , by partition the information system to information granule and mapping granule object sets, the algorithm reduced the number of database scanning and reduced object sets required when computing support of candidate item, in this way we improved the efficient of running. For the dataset of small support with high complexity, it is more applicable. The experiment results show: the proposed method is effective.
['Yueqin Zhang', 'Qingwei Yan', 'Lili Zong']
An Association Rule Algorithm Based on Quotient Space