In this paper, we propose an efficient frequent-pattern-based outlier detection method, namely, UWFP-Outlier, for identifying the implicit outliers from uncertain weighted data streams. For reducing the time cost of the UWFP-Outlier method, in the weighted frequent pattern mining phase, we introduce the concepts of the maximal weight and maximal probability to form a compact anti-monotonic property, thereby reducing the scale of potential extensible patterns. For accurately detecting the outliers, in the outlier detection phase, we design two deviation indices to measure the deviation degree of each transaction in the uncertain weighted data streams by considering more factors that may influence its deviation degree; then, the transactions which have large deviation degrees are judged as outliers. The experimental results indicate that the proposed UWFP-Outlier method can accurately detect the outliers from uncertain weighted data streams with a lower time cost.

在本文中，我们提出了一种有效的基于频繁模式的离群值检测方法，即UWFP-Outlier，用于从不确定的加权数据流中识别隐式离群值。为了减少UWFP-Outlier方法的时间成本，在加权频繁模式挖掘阶段，我们引入了最大权重和最大概率的概念形成紧凑的抗单调性，从而减小了潜在可扩展图案的规模。为了精确地检测离群值，在离群值检测阶段，我们设计了两个偏离指数，通过考虑更多可能影响其偏离度的因素来衡量不确定加权数据流中每个事务的偏离度。然后，将偏离度大的交易判断为离群值。实验结果表明，所提出的UWFP离群值方法可以准确地从不确定的加权数据流中检测离群值，且时间成本较低。