离异点是偏离部分观察对象的数据点，根据离异点所在单元的密度与相邻单元的密度相比可能偏高或偏低的特点，本文提出了基于网格相邻关系的离异点识别算法GAO。该算法用单元间的相对密度和单元质心距离来衡量单元间的离异度，根据离异度确定离异单元和离异点。实验结果表明，该算法能有效地识别出多密度数据集的离异点，算法的效率优于Cellbased算法，且适合大数据集的离异点识别。

Outliers are the deviation objects of data points. The paper presents an outlier recognition algorithm based on grid adjacency relation (GAO), according to the high or low density of the outlier unit comparing to its neighborhood. The outlier and the outlier unit are determined by the degree of deviation, which is measured by the relative density and distance of the center of mass between units. The experimental results show that the algorithm can recognize the outlier of multidensity, highdimensional and large data sets effectively. The algorithm’s efficiency is better than that of the Cellbased algorithms.