Based on the idea that objects that are far away from the others are outliers, proximity-based approaches assume the proximity of an outlier deviates significantly from that of most of the others in the data set.
    There are two types of proximity-based outlier detection methods

    • Distance-based outlier detection: An object o is an outlier if its neighbourhood does not have enough other points in it
    • Density-based outlier detection: An object o is an outlier if the density of objects around it is much lower than that of its neighbours

    基于远离其他对象的对象是异常值的想法，基于邻近度的方法假设异常值的邻近度显著偏离数据集中大多数其他对象的邻近度。
    有两种基于邻近度的异常检测方法

    • 基于距离的离群点检测:如果一个对象的邻域中没有足够的其他点，则该对象是离群点
    • 基于密度的离群点检测:如果一个物体周围的物体密度比它的邻居低很多，那么这个物体就是离群点