HewaNadungodage, ChandimaXia, YuniLee, Jaehwan JohnTu, Yi-Cheng2025-04-112025-04-112013Hewanadungodage C, Xia Y, Lee JJ, Tu YC. Hyper-structure mining of frequent patterns in uncertain data streams. Knowl Inf Syst. 2013;37(1):219-244. doi:10.1007/s10115-012-0581-yhttps://hdl.handle.net/1805/46990Data uncertainty is inherent in many real-world applications such as sensor monitoring systems, location-based services, and medical diagnostic systems. Moreover, many real-world applications are now capable of producing continuous, unbounded data streams. During the recent years, new methods have been developed to find frequent patterns in uncertain databases; nevertheless, very limited work has been done in discovering frequent patterns in uncertain data streams. The current solutions for frequent pattern mining in uncertain streams take a FP-tree-based approach; however, recent studies have shown that FP-tree-based algorithms do not perform well in the presence of data uncertainty. In this paper, we propose two hyper-structure-based false-positive-oriented algorithms to efficiently mine frequent itemsets from streams of uncertain data. The first algorithm, UHS-Stream, is designed to find all frequent itemsets up to the current moment. The second algorithm, TFUHS-Stream, is designed to find frequent itemsets in an uncertain data stream in a time-fading manner. Experimental results show that the proposed hyper-structure-based algorithms outperform the existing tree-based algorithms in terms of accuracy, runtime, and memory usage.en-USPublisher PolicyData miningData streamData uncertaintyFrequent patternsArticle