Efficient mining probabilistic frequent itemset in uncertain databases
一种有效的不确定数据概率频繁项集挖掘算法

The way to calculate the frequentness probability in PFIM limited its applications, it needed to scan the database for many times and generated a large number of candidate sets. This paper proposed a new algorithm named EPFIM. First, the new method of calculating the frequentness probability made it easier to update frequentness probability of itemset, and could be adapted in more situations. Second, it used uncertain probability matrix to store the database in order to scan database less. In addition, the sequence of items and deleting unwanted transactions gradually improved efficiency of mining. Theoretical analysis and experimental results show EPFIM performances better.