To improve the time and space efficiency of Frequent Pattern (FP) mining algorithm over uncertain dataset, the Uncertain Frequent Pattern Mining based on Max Probability (UFPM-MP) algorithm was proposed. First, the expected support number was estimated using maximum probability of the transaction itemset. Second, by comparing this expected support number to the minimum expected support number threshold, the candidate frequent itemsets were identified. Finally, the corresponding sub-trees were built for recursively mining frequent patterns. The UFPM-MP algorithm was tested on 6 classical datasets against the state-of-the-art algorithm AT (Array based tail node Tree structure)-Mine with positive results (about 30% improvement for sparse datasets, and 3-4 times more efficient for dense datasets). The expected support number estimation strategy effectively reduces the number of sub-trees and items of header table, and improves the algorithm's time and space efficiency; and when the minimum expected support threshold is low or there are lots of potential frequent patterns, time efficiency of the proposed algorithm performs more remarkably.

%U http://www.joca.cn/EN/10.11772/j.issn.1001-9081.2015.07.1921