提出了一种基于局部孤立系数(loc)的孤立点挖掘算法。
This paper presents a Local Outlier Coefficient - Based (LOC) Mining of Outliers.
这种方法使用了基于密度的孤立点挖掘的主要思想，用克隆选择算法进行数据立方体搜索。
It is a dense-based method and the low-dense data cubes are searched by clonal selection algorithm.
孤立点挖掘又称孤立点分析、异常检测、例外挖掘、小事件检测、挖掘极小类、偏差检测。
The problem of outlier mining has been variously called outlier analysis, anomaly detection, exception mining, detecting rare events, mining rare classes, deviation detection, etc.
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