UF-evolve: Uncertain frequent pattern mining

Many frequent-pattern mining algorithms were designed to handle precise data, such as the FP-tree structure and the FP-growth algorithm. In data mining research, attention has been turned to mining frequent patterns in uncertain data recently. We want frequent-pattern mining algorithms for handling uncertain data. A common way to represent the uncertainty of a data item in record databases is to associate it with an existential probability. In this paper, we propose a novel uncertain-frequent-pattern discover structure, the mUF-tree, for storing summarized and uncertain information about frequent patterns. With the mUF-tree, the UF-Evolve algorithm can utilize the shuffling and merging techniques to generate iterative versions of it. Our main purpose is to discover new uncertain frequent patterns from iterative versions of the mUF-tree. Our preliminary performance study shows that the UF-Evolve algorithm is efficient and scalable for mining additional uncertain frequent patterns with different sizes of uncertain databases.