Hyper-structure mining of frequent patterns in uncertain data streams.
Hyper-structure mining of frequent patterns in uncertain data streams.
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DOI：
10.1007/s10115-012-0581-y
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发表时间：
2013-10-01
影响因子：
2.7
通讯作者：
Tu, Yi-cheng
中科院分区：
文献类型：
--
作者：
HewaNadungodage, Chandima;Xia, Yuni;Lee, Jaehwan John;Tu, Yi-cheng
数据不确定性是许多现实应用中固有的，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，无限的数据流。近年来，新的方法已经开发出在不确定数据库中发现频繁模式，然而，在不确定数据流中发现频繁模式的工作非常有限。目前不确定流中频繁模式挖掘的解决方案采用基于FP树的方法;然而，最近的研究表明，基于FP树的算法在数据不确定性的存在下表现不佳。在本文中，我们提出了两个超结构为基础的假阳性导向的算法，有效地挖掘频繁项集的不确定数据流。第一个算法，UHS-Stream，被设计为找到所有的频繁项集到当前时刻。第二个算法TFUHS-Stream，被设计为在不确定的数据流中以时间衰落的方式发现频繁项集。实验结果表明，所提出的基于超结构的算法优于现有的基于树的算法在准确性，运行时间和内存使用。
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.
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