基于动态聚类与轨迹分析的行为漂移无监督检测
Unsupervised Detection of Behavioural Drifts with Dynamic Clustering and Trajectory Analysis
AI总结：
针对行为漂移异常检测难度大、研究少的问题，提出无监督实时检测算法DynAmo，结合动态聚类、轨迹特征提取与集成散度检验，实现行为序列漂移时段的实时识别。
摘要：
过去几十年间，人类行为实时监测（尤其是在电子健康应用中）一直是活跃的研究领域。在基于物联网的传感环境之上，研究人员已提出多种异常检测算法用于异常情况的早期发现。渐变过程（通常被称为漂移异常）在现有文献中受到的关注要少得多，因为相较于突发的临时性变化（点异常），它们对应的场景更具挑战性。本文首次提出了一种名为DynAmo的完全无监督实时漂移检测算法，能够在漂移发生时识别出漂移时段。DynAmo包含一个动态聚类组件用于捕捉被监测行为的整体趋势，以及一个轨迹生成组件，用于从密度最高的聚类质心中提取特征。最后，我们在滑动参考窗口和检测窗口上应用集成散度检验，以检测行为序列中的漂移时段。
英文摘要：
Real-time monitoring of human behaviours, especially in e-Health applications, has been an active area of research in the past decades. On top of IoT-based sensing environments, anomaly detection algorithms have been proposed for the early detection of abnormalities. Gradual change procedures, commonly referred to as drift anomalies, have received much less attention in the literature because they represent a much more challenging scenario than sudden temporary changes (point anomalies). In this paper, we propose, for the first time, a fully unsupervised real-time drift detection algorithm named DynAmo, which can identify drift periods as they are happening. DynAmo comprises a dynamic clustering component to capture the overall trends of monitored behaviours and a trajectory generation component, which extracts features from the densest cluster centroids. Finally, we apply an ensemble of divergence tests on sliding reference and detection windows to detect drift periods in the behavioural sequence.
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