Review of Algorithm for Mining Frequent Patterns from Uncertain Data

Mining frequent patterns from traditional database is an important research topic in data mining and researchers achieved tremendous progress in this field. However, with high volumes of uncertain data generated in distributed environments in many of biological, medical and life science application in the past ten years, researchers have proposed different solutions in extending the conventional techniques into uncertainty environment. In this paper, we review the classic mining algorithms: Apriori algorithm and FP-growth algorithm, and then analyses the improved algorithm for mining frequent patterns form uncertain data and uncertain data streams. Last, some further research directions on mining frequent patterns form uncertain data are given.