Fast Algorithms for Frequent Itemset Mining from Uncertain Data

The majority of existing data mining algorithms mine frequent item sets from precise databases. A well-known algorithm is FP-growth, which builds a compact FP-tree structure to capture important contents of the database and mines frequent item sets from the FP-tree. However, there are situations in which data are uncertain. In recent years, researchers have paid attention to frequent item set mining from uncertain databases. UFP-growth is one of the frequently cited algorithms for mining uncertain data. However, the corresponding UFP-tree structure can be large. Other tree structures for handling uncertain data may achieve compactness at the expense of looser upper bounds on expected supports. To solve this problem, we propose two compact tree structures which capture uncertain data with tighter upper bounds than existing tree structures. We also designed two algorithms that mine frequent item sets from our proposed trees. Our experimental results show the tightness of bounds to expected supports provided by these algorithms.