We study the incremental data mining technology based outlier factor.
本文主要研究了基于孤立点因子的增量式挖掘技术。
It uses local outlier mining method to count the Local Outlier Factor(LOF) of the outlier candidated object and detects anomaly attacks.
采用局部离群挖掘方法计算离群候选对象的离群因子，检测出异常攻击。
The LOF (local outlier factor) algorithm is a very distinguished local outlier detecting method, which assigns each object an outlier-degree value.
LOF算法是一个著名的局部离群点查找方法，该方法赋予了表征每一个空间点偏离程度的数值。
The distance definition for mixed attribute, the outlier factor measured outlier degree of an object and unsupervised intrusion detection method are proposed.
提出了一种适用于混合属性的距离定义和度量对象异常程度的异常因子，由此提出了一种无指导的入侵检测方法。
The algorithm can indicate the degree of outlier with the local deviate factor, so the outlier can be identified exactly and the precision is measurable.
同时用局部偏离指数指示离群点的偏离程度，又具有识别精度高和偏离程度可度量的优点。
According to the characteristics of spatial data sets, this paper proposes an outlier detection algorithm based on the Space Local Deviation Factor (SLDF).
针对空间数据集的特性，提出一种基于空间局部偏离因子(SLDF)的离群点检测算法。
According to the characteristics of spatial data sets, this paper proposes an outlier detection algorithm based on the Space Local Deviation Factor (SLDF).
针对空间数据集的特性，提出一种基于空间局部偏离因子(SLDF)的离群点检测算法。
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