DISC: Efficient Uncertain Frequent Pattern Mining with Tightened Upper Bounds
Richard Kyle MacKinnon, Teagan D. Strauss, Carson Kai-Sang Leung · 2014
UF-growth is a tree-based exact algorithm for mining frequent patterns from uncertain data. While it directly calculates the expected support of an item set, it requires a significant amount of storage space to capture all existential probability values among the items. To eliminate the extra space requirement of UF-growth, the CUF-growth algorithm combines nodes with the same item by storing an upper bound on expected support. In this paper, we introduce two new algorithms for achieving a tighter upper bound than CUF-growth, and we evaluate the trade-off between storing more information to further tighten the bound and its effect on the performance of the algorithm. Experimental results show the effectiveness of our algorithms.