Data 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.

中文翻译：

---

不确定数据流中频繁模式的超结构挖掘。

数据不确定性是许多实际应用中固有的，例如传感器监控系统、基于位置的服务和医疗诊断系统。此外，许多现实世界的应用程序现在能够产生连续的、无限制的数据流。近年来，已经开发了新的方法来在不确定的数据库中找到频繁的模式；然而，在发现不确定数据流中的频繁模式方面所做的工作非常有限。当前不确定流中频繁模式挖掘的解决方案采用基于 FP-tree 的方法；然而，最近的研究表明，基于 FP-tree 的算法在存在数据不确定性的情况下表现不佳。在本文中，我们提出了两种基于超结构的面向误报的算法，以有效地从不确定数据流中挖掘频繁项集。第一个算法 UHS-Stream 旨在查找当前时刻的所有频繁项集。第二种算法，TFUHS-Stream，旨在以时间衰落的方式在不确定的数据流中找到频繁项集。实验结果表明，所提出的基于超结构的算法在准确性、运行时间和内存使用方面优于现有的基于树的算法。