Mining frequent patterns from univariate uncertain data

In this paper, we propose a new algorithm called U2P-Miner for mining frequent U2 patterns from univariate uncertain data, where each attribute in a transaction is associated with a quantitative interval and a probability density function. The algorithm is implemented in two phases. First, we construct a U2P-tree that compresses the information in the target database. Then, we use the U2P-tree to discover frequent U2 patterns. Potential frequent U2 patterns are derived by combining base intervals and verified by traversing the U2P-tree. We also develop two techniques to speed up the mining process. Since the proposed method is based on a tree-traversing strategy, it is both efficient and scalable. Our experimental results demonstrate that the U2P-Miner algorithm outperforms three widely used algorithms, namely, the modified Apriori, modified H-mine, and modified depth-first backtracking algorithms.