SDO是一种利用基于距离的异常值估计对数据样本进行评分的算法。
SDO is an algorithm that scores data samples with estimations of distance-based outlierness.
与其他异常值检测算法一样，SDO也是一种渴望学习的算法，它在训练阶段创建数据集的低密度模型，然后将新样本与创建的模型进行比较。
Alike other outlier detection algorithms, SDO is an eager learner that creates a low-density model of the dataset during a training phase and later compares new samples with the created model.
这种方案允许在应用程序阶段减轻计算负载，而不需要重新调用旧的数据样本。
Such scheme allows lightening the computational load during application phases, not requiring to recall old data samples again.
SDO被设计成嵌入到自主操作的系统或框架中，并且必须以连续的方式处理大量数据。
SDO is devised to be embedded in systems or frameworks that operate autonomously and must process large amounts of data in a continuos manner.
SDO可用于大数据和流数据应用的机器学习解决方案。
SDO is a machine learning solution for Big Data and stream data applications.
参考文献：
F. Iglesias, T. Zseby, A. Zimek. Outlier Detection Based on Low Density Models. Proc. IEEE International Conference on Data Mining Workshops, ICDM Workshops, Singapore; 11-17-2018 – 11-20-2018. pp. 970 – 979.
完整源码下载请点击“阅读原文”