一种基于层次聚类的全局孤立点识别方法*

现有的孤立点检测算法在通用性、有效性、用户友好性及处理高维大数据集的性能还不完善，为此提出一种快速有效的基于层次聚类的全局孤立点检测方法。该方法基于层次聚类的结果，根据聚类树和距离矩阵可视化判断数据孤立程度，并确定孤立点数目。从聚类树自顶向下，无监督地去除孤立点。仿真实验验证了方法能快速有效识别全局孤立点，具有用户友好性，适用于不同形状的数据集，可用于大型高维数据集的孤立点检测。

Global outlier detection based on hierarchical clustering

The existing outlier detection algorithms should be improved due to their versatility,effectiveness,user-friendliness,and the performance in processing high-dimensional and large databases.A fast and effective hierarchical clustering based global outlier detection approch is proposed in this paper. Agglomerative hierarchical clustering is performed firstly,and then the isolated degree of the data can be visually judged and the number of the outliers can be determined based on the clustering tree and the distance matrix.After that, the outliers is identified unsupervisedly from the top to down of the clustering tree.Experimental results show that,this approch can identify global outliers fastly and effectively,and is user-friendly and capable at datasets of various shapes.Experiments also illustrate that this approach is suitable for use on high-dimensional and large databases.