MAC093C-0-FS-3-C-130-B-1 控制模块

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Breuning 等学者定义了局部异常因子的概念，提出了一种基于密度的局部异常点挖掘算法，它是用局部异常因子 LOF(Local Outlier Factor)表征数据对象与它的邻居对象的偏离程度，LOF越大则认为该点是异常数据的可能性越大。ToWel等人在分析神经网络的基础上，根据神经网络的理论提出了基于神经网络的异常数据挖掘算法。

最近提出的比较流行的异常数据挖掘算法是基于核的分类方法，主要思想是将输入的数据通过关系函数映射到一个高维特征空间，通过高维空间的分类超平面，就可以建立一个简单的分类模型，从而区分正常或异常数据。