Analyzing sequence data usually leads to the discovery of interesting patterns and then anomaly detection. In recent years, numerous frameworks and methods have been proposed to discover interesting patterns in sequence data as well as detect anomalous behavior. However, existing algorithms mainly focus on frequency-driven analytic, and they are challenging to be applied in real-world settings. In this work, we present a new anomaly detection framework called DUOS that enables Discovery of Utility-aware Outlier Sequential rules from a set of sequences. In this pattern-based anomaly detection algorithm, we incorporate both the anomalousness and utility of a group, and then introduce the concept of utility-aware outlier sequential rule (UOSR). We show that this is a more meaningful way for detecting anomalies. Besides, we propose some efficient pruning strategies w.r.t. upper bounds for mining UOSR, as well as the outlier detection. An extensive experimental study conducted on several real-world datasets shows that the proposed DUOS algorithm has a better effectiveness and efficiency. Finally, DUOS outperforms the baseline algorithm and has a suitable scalability.
翻译:分析序列数据通常导致发现有趣的模式,然后发现异常现象。近年来,提出了许多框架和方法,以发现序列数据中的有趣模式,并发现异常行为。然而,现有的算法主要侧重于频率驱动分析,在现实世界环境中应用是具有挑战性的。在这项工作中,我们提出了一个称为DUOS的新的异常探测框架,它能够从一组序列中发现具有实用性认知的异常序列规则。在这个基于模式的异常检测算法中,我们既采用了一个组的异常特征和实用性,又引入了实用性认知异常序列规则(USR)的概念。我们表明,这是发现异常现象的更有意义的方法。此外,我们建议了一些高效的运行战略,用于开采UOSR,以及外部检测。对几个真实世界数据集进行的广泛实验研究表明,拟议的DUOS算法具有更好的有效性和效率。最后,DUOS的算法超越了基线和适当尺度。