Fast Algorithms for Frequent Itemset Mining from Uncertain Data

The majority of existing data mining algorithms mine frequent itemsets from precise data. A well-known algorithm is FP-growth, which builds a compact FP-tree structure to capture important contents of precise data and mines frequent itemsets from the FP-tree. However, there are situations in which data are uncertain. To capture important contents (e.g., existential probabilities) of uncertain data for mining frequent itemsets, the UF-growth algorithm uses a UF-tree structure. However, the UF-tree 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 fast algorithms that use compact tree structures for capturing uncertain data with tightened upper bounds to expected support (tube) for frequent itemset mining from uncertain data. Experimental results show the tightness of tube provided by our algorithms and the compactness of our tree structures.