In the data analysis of commercial, medical etc, the database that consist of some transactions we don’t know if they will appear or not callded uncertain database, the occurrence of data is characterized as discrete random variables and thus represented by probability distributions. Association rules mining from uncertain databases is one of the hot problems from data mining. Aming at the characteristics of uncertain database, this paper proposes a disjunctive rules mining algorithm called DRUD. The algorithm first to select all possible pairs of frequent itemsets, comparing the minimum support, and then extract the effective disjunctive rules. Simulation show that ,compared with UApriori and PFCIM, the confidence of the rules generated by algorithm DRUD has improved, the run time of DRUD also hao improved, so the new algorithm DRUD is more applicable to the massive uncertain database ming applications.
