Probabilistic Frequent Itemsets Mining Based on Expectation Bound over Uncertain Database
Lihai Nie, Zhiyang Li, Heng Qi, Weijiang Liu, Wenyu Qu · 2017
Frequent itemsets discovery is popular in database communities recently. Because real data is often affected by noise, in this paper, we study to find frequent itemsets over probabilistic database under the Possible World Semantics. It is challenging because there may be exponential number of possible worlds for probabilistic database. Although several efficient algorithms are proposed in the literature, it is hard to mine frequent itemsets in large uncertain database due to the high time consuming. To address this issue, we propose an efficient algorithm to mine probabilistic frequent itemsets. A pruning strategy is also presented to accelerate the process of generating candidates. Extensive experiments have been done on synthetic and real databases, demonstrating that the proposed method preforms better than state-of-art methods in most cases.