Outlier detection algorithm based on shared nearest neighbor
基于共享最近邻的离群检测算法

This paper introduced an outlier detection algorithm based on the shared nearest neighbor clustering in order to detect the outliers with the mixed attributes. The algorithm calculated the shared nearest neighbor similarity measure between result clusters caused by the incremental clustering. It could not only find the arbitrary shape clusters but also identify the global outlier in large and high-dimensional dataset with different density. Presented approach had nearly linear time complexity with the number of attributes and the size of dataset which results in good scalability.